The AI Optimist

AI helps you know what your customers want, their questions, and why they buy. If you stop doing things the old way - like search is the center of your customer’s universe - and follow this plan.

Because 90% of the new business game is showing up with human-generated content!'

Does AI Reveal New Customers for a Window Tinting Company?

A window tinting company was losing business. They didn’t know what to do and went to Jasper AI.

While I don’t know their prompt, it likely went something like this:

As a business consultant for the window tinting business, identify and define five possible customer personas. 

Then, include a specific marketing strategy, including why they would use our window tinting products.

From these results came the best answer: families with children to protect them from harmful UV rays.

The company never thought of it. Sales skyrocketed to this new market that they never considered. 

Being in business for a long time didn’t help; it stopped them from considering options.

* They could have searched and gotten tons of opinions. Paying consultants? It’s the same as a search.

* AI gave them options, which is the key to understanding the new world of AI-powered search.

* It’s less about organic placements, as they are rare, and more about using AI to know your customers beyond keywords. 

* Intention begins by understanding who, what, where, why, and when customers act.

Apply that to your content on social media, and you’ll get better search results.

While this sounds like the end of the world, my experiment at The AI Optimist proves that this multichannel approach is vital to succeeding.

Instead of overfocusing on search only, I look for new patterns—intention rather than trying to impress with my knowledge of AI, which my competitors do endlessly.

In a few months of being active, The AI Optimist gets: 

* 25% of traffic from Google search. 

* 25% direct visitors (many from social who type it in or via referrals); 

* 25% from LinkedIn, and 

* 12% from Apple’s iTunes (think Google search is arduous, try Apple!)

How am I doing it?

With a blend of old-school SEO content marketing, social posts that don’t share links to my content, email marketing, and showing up every week on schedule with my podcast. 

The old days of posting your links on social for traffic are dead. Post them in the comments, if anywhere.

Social post-sharing links reduce exposure by almost every social platform algorithm.

The impact of AI on search goes way beyond the old meaning of search.

ChatGPT, Bard, and Claude are already grabbing people who used to search—giving them an option not to go to all those sites listed in search results or from paid ads.

2 Search Opportunities – Organic or 🥕 GMO = Google Monetized Opportunities, including their new SGE (Search Generative Experience), paid ads, YouTube, and YouTube ads.

Here’s Google’s SGE promo video; WOW, look at this!

While many predictions of the future impact of AI point to possible events, the impact on search is already here. Consider the facts:

* SEMRush did a study in May 2022, looking then at “Zero Click results” on search. That means that someone searched and did not leave Google but got their answers immediately without clicking.

* 57% of mobile searches and 25% of desktop searches were zero clicks. The SEMRush data is an early study and needs to be more definitive. Yet it points out, before the rise of ChatGPT, an obvious fact: search is changing, and we all have to adapt.

* Looking at Google’s vision of future search – SGE (Search Generative Experience) shows how ads, AI, YouTube, and other content buried the old ideal of organic traffic.

While no one knows where this is going, here’s the truth: organic search will lose even more ground in Google search. And where Google goes, Bing will follow.

“SGE is coming, and the worst thing you could do is nothing. You need to lay the groundwork now: sources inside Google tell us that SGE will be rolled out in limited circumstances to a small sample (<1%) of users in Q4. 

Google will use this to collect additional data and tweak the product, but competitive pressure from Bing and ChatGPT will likely force a widespread rollout in 2024.

CEOs and founders should ensure that their org has a plan and knows what the impact might be on achieving growth goals in 2024. 

Marketing leaders need to direct their teams to develop the approaches and testing to begin to understand the real impact and how to respond to these major changes. 

So, what should you do today to prepare for this zero-click world? 

There are no definitive answers right now, so the best advice we have is to be engaged, proactive, and real.”

https://www.insightpartners.com/ideas/generative-ai-seo-zero-click-searches/

It shows a comparison between traditional Google search results and a predictive next generation of Google’s search results:

* The Old Way - Traditional Google Search Results (Left Side)

* The current format starts and ends with paid ads. These are highlighted in pink, showing sponsored results.

* Sandwiched between these ads are organic search results, shown in green. These are the ones Google’s algorithm deems most relevant to the search - organic traffic.

* Next Generation Search Generative Results (Right Side)

* The new format features a domination of paid results at the top and bottom, similar to the current design.

* Below the paid ads section are SGE – Search Generative Experience - AI results. SGE is also to the right of organic results.

* There are fewer organic search results and fewer chances for websites to get organic traffic.

* Another section of ads interrupts organic, followed by a smaller group of organic results and another area of paid ads.

 (See the pattern? Google Ads, AI, organic, Ads, organic; this does not look good for the old ways of free search traffic.)

* Below are the YouTube results. Given that Google owns YouTube, this provides a video element for searching.

Pros:

* Expanded Results: Different types of content, like YouTube videos and AI-generated responses, offer multimodal sources of results sandwiched by ads.

* Relevance: AI-curated results from extensive data and advanced algorithms improve the search experience.

Cons:

* Reduces Organic Traffic: Organic space is shrinking, pushing businesses to buy ads and learn how to deliver relevant answers that AI picks up on.

* Commercial Focus: Paid ads already dominate organic; will this eventually erode trust?

* Over-Reliance on AI: One day, somebody will fix AI’s bias and hallucination aspect.

Parallel Worlds: In the old days of search, results began with relevance and popularity. 

Ads began to push organic down a few years ago. Now, add AI-curated summaries and videos. Why leave Google or Bing at all?

Variety is key here. Yet this will also challenge ad revenue, as zero clicks impact clicks on paid ads, which keeps Google rich. 

Key Takeaways: Google’s predicted search results lean towards a commercial, varied, and AI-centric approach. 

 Organic is getting buried? 🥑

 Whatever it becomes, this trend of fewer clicks from search is definitely on the top of mind for many I’m helping. People stuck in the past wonder why Google is reducing organic results.

Because they get paid by clicks on ads. They get paid for SGE (GenAI) results. They get paid by YouTube. Organic doesn’t produce.

BEGIN WITH CUSTOMER INTENTION – APPLY THIS TO SEARCH, SOCIAL, AND COPY

The basics of SEO that work has, and will be E-E-A-T:

None of this applies if you try to use AI to write everything for you. Business and search are notorious for copycats. 

AI learned from the copycats, so the content you get isn’t as good as the information, intent of your customers, and strategy AI can share.

You need to show Google and your customers:

* E-xpertise: show in-depth and relevant knowledge. The old listicles and generic articles won’t help anymore. That’s what people are creating with AI. Show you know what you’re talking about.

* E-xperience: Real-life case studies and unique points of view are not AI’s strengths. Your competition is taking shortcuts, and you stand a chance by being authentic. Again, don’t tell, show them!

* A-uthority: Make your site a source for the markets and topics you focus on. Get people to link to your content. And if you have a podcast, interview leaders, and share links, it’s a way to network and build your authority.

* T-rust: Use reliable sources (hopefully your own experience) to make your content accurate.

Plus one more - be consistent. Post regularly, at least once a week. More is helpful in the early days. I post this podcast once a week, and the growth is steady. 

 THE NEW SEARCH – LLMS, SOCIAL MEDIA, AND VIDEO

 Beyond just Google, consider the new types of searches:

 🌶 LLMs - ChatGPT, Claude, Bard, Poe, and many others grabbing attention.

*  How often have you gone to ChatGPT to get an answer instead of Google? 

By aggregating search results, it’s a no-brainer to use this for so much research into travel and what to do, and instead of sifting through links, get an answer.

Check for accuracy (notice how we all repeat this), but it’s not bad.

* Consider what I’ve been able to do within the past few months:

* Deliver an electrical plan to my electrician with no knowledge that saved me at least $5,000;

* Create a green screen room for video shoots. It is still in development, but I have a plan.

* I researched numerous AI regulations and got summaries and AI technical questions that allowed me to understand some of the issues without being a data scientist. 

Doing these on search would force me to collate and organize resources. A few questions and checking facts lead me to a faster solution, far more efficient than a search.

AI alone will impact search volume dramatically. While Google and Bing are trying to integrate it into search, watching if that’s needed will be interesting. Most use AI for discussions, not surfing, but this is super early, so watch what happens.

A search seems so 2018. 

Plus, everyone is gaming and competing with similar content for many top terms. 

Those who use AI are creating clone-like content.

I’m suggesting you do your writing, and if you use AI, use plagiarism checks like Originality.ai and Grammarly.

 🌶Influencer Search and TikTok vids - GenZ uses them to search and others.

 Influencers on social media are becoming the community searches people do on social. Ask a friend or find influencers and see what they recommend as an alternative to search.

For GenZ, TikTok has a robust e-commerce influence; using Similar Web in 2022 for Q1 and Q2, many clicks go to Skimlinks and Amazon, both of whom have affiliate programs that are easy to set up and share a link.

GenZ is using TikTok as its search engine. And it’s not just them.

“In the last decade, though, the world’s fastest-growing marketplaces have cropped out of this China-laid groundwork. In the fiscal year 2021 (ending March 31, 2021), Alibaba Group’s revenue was RMB 717.3 billion (approximately $109.5 billion), according to the company’s annual report, growing at a 31% CAGR over the last three years. 

Shein, the largest global fast fashion company, exploded over the last three years, outpacing Zara, Forever21 and H&M, raking in $15.7 billion in revenues in 2021 and growing at a CAGR of 49% over the last three years. 

And lastly, PinDuoDuo just launched its cross-border marketplace, Temu, and did $14.7 billion top line, growing at a CAGR of 131% over the last five years, from $300 million to $18 billion. 

So what’s changed? American tech companies’ ability to capture attention. 

Until now, platforms like Google, Facebook, Instagram and Amazon maintained status as the world’s most trafficked websites. 

And as the most trafficked websites, filled with social media and products, these companies have controlled discovery and captured a cut of the transaction of slinging largely Chinese goods. “

https://www.glossy.co/fashion/op-ed-the-real-tiktok-threat-is-in-shops-and-cross-border-commerce/

Looking at the financial figures coming out of TikTok, you understand that, unlike any other social platform, people engage and buy things directly on TikTok and from links off TikTok.

While this happens on other social platforms, it’s not close to the volume.

AI’s TikTok role? Check the algorithm; it’s addictive and embedded, not featuring single influencers but sharing the trends.

Shein, for example, scrapes social media for fashion ideas, trends, and products people buy. And those products move because they can quickly create them within a week.

Some have accused Shein of stealing their ideas; their data-driven AI is years ahead of others in the fashion industry, though many new companies are competing.

It used to be that Google was one of the only high-intent places to market; now TikTok is oddly becoming bigger than Google Search. 

Time will tell, but social media is not what it used to be, and much of that is due to AI algorithms that know, follow, and embed buying behaviors.

 🌶 YouTube - yes, it’s Google and its search power; the Tiktokification spreads to the short vids that have grown in the past year.

YouTube is also a search engine for this content; many go there to learn or find products. 

The impact of short videos is felt across the social media spectrum and won’t die down.

 🌶 And, of course, Facebook, Instagram, Snapchat, and X are all used as semi-search engines to varying degrees. 

It’s the idea of friends as influencers, with retargeting ads and deep knowledge of behaviors guiding people to purchase in ways they never did.

Many searches for what they like (intention found in hashtags), follow influencers, ask friends, and often don’t click like they used to when going to a site.

 Paid ads are often the way in, with a steep price in search because of the targeting and intent involved. 

But if you want to grow in business, doing some paid ads is a must.

Podcasts Give You Audio, Video, and Written Content

With a podcast, most quit their shows after 10-20 episodes. It takes time and money if you need instant results. 

Even then, time and repeat episodes earn you a following you most likely cannot buy.

Those who keep doing their podcast get remembered more than those who quit and try other shortcuts.

* Podcasts allow me to have video, audio, and written content. I click a button on Substack; I get a free transcript.

* I import that audio into Adobe Premiere for video, and it creates accurate captions automatically. Even Slack does this now; transcriptions are free and available. You need them for videos!

* Plus, numerous tools will take your audio and break it into short snippets and videos with headlines, descriptions, and links.

PROMPT POWER

Simple SEO and Content Marketing Strategy with Prompts

Begin with the end in mind: How do you position yourself, what are the keywords, and what are the intentions and needs of your audience?

Ask AI by taking the following and getting into a discussion for 30-60 minutes.

Here is a sequence of prompts to get started, and there are tons of examples online.

SEO Positioning 

Create a list of SEO keywords for (insert your product or business). Include 2-3 keyword phrases, then a separate section for long tail keywords.

INTENT 

Why do people search for these keywords? Define the reasons behind their searches, including what they are confused about and possible answers they would be seeking. 

As an SEO agency professional, develop a list of 10 popular questions related to (keyword) or (topic of your business)

STRATEGY

Define seven keyword strategies for (insert your business and location if relevant for local SEO)

Develop a content marketing strategy for next year using the top keywords and methods mentioned above.

PODCAST OR BLOG POST TITLES TO DEVELOP

Write ten blog post titles based on the content suggested above.

Write an outline, summary, and subheadings for your blog posts.

SOCIAL MEDIA POSTS

Write ten social media posts based on the content suggested above. Include 1 for LinkedIn of 500 characters, one of 280 characters for X/Twitter (much more if you pay), 500 words for Facebook, etc.

Write social media headlines that engage users and encourage interactivity for each post.

Create Your Content with These Ideas!

It’s easy to ask AI to write it, and that’s one path. SEO has already been a copycat game for years, and the content coming out is often directly plagiarized from another article.

Writing has positively impacted clickthrough rates, as GenAI has no emotions and is not a human. 

Creating original content is your strength and opportunity; you get all you need in a few minutes to focus on the writing.

If you do this through GenAI and edit it, you’ll spend about the same time editing as you would writing, and it won’t be original nor stand out.

Get Out on Social and Communities – Build Your List and don’t rely on social platforms.

Social is the place to be, but with the shifting tides, beware. 

For example, Facebook’s algorithm has gotten messy the past month, shutting down massive amounts of businesses that have been using the platform legitimately for years.

I know several people who’ve had ten years of content and followers wiped out without doing anything wrong. All because the algorithm has gone batty; this is no joke.

You don’t own social followers, and building your community is critical.

* Do this with an email list; it’s the best way and a great asset for your business.

* Build your community on Slack, Discord, Circle, or other places, though understand that getting people to use them frequently is tricky.

* It would help if you made it a place they want to return to and connect with people. 

Navigating the shifting landscape of organic search visibility requires websites to be adaptable and strategic. 

Given the predicted (key here is no one knows) changes to Google’s search results, here are some strategies websites can consider:

1. Diversify Traffic Sources:

* Social Media: Engage audiences on platforms like Facebook, TikTok, Twitter, LinkedIn, and Instagram. Share content, interact with followers, and run targeted ad campaigns.

* Email Marketing: Build an email subscriber list. Regular newsletters with valuable content can drive consistent traffic.

* Collaborations & Guest Posting: Collaborate with other websites, influencers, and industry leaders to tap into their audience.

2. Optimize for AI and Be Relevant:

* Produce relevant content over time, ensuring consistent traffic irrespective of changing trends.

* Google often displays these “position zero” AI results, and sometimes snippets, above all other organic listings. Ensuring content answers common questions briefly can improve the chances of being featured.

3. Video Content:

* Given the prominence of YouTube results, creating and optimizing video content becomes vital. Ensure video titles, descriptions, and tags are SEO-friendly.

4. Get Good at Paid Advertising:

* Since paid results still dominate, allocate a budget for Google Ads. Regularly analyze and optimize campaigns for better ROI.

* Improve User Experience (UX):

* Google factors in user experience when ranking websites. Ensure fast loading times, mobile optimization, and intuitive site navigation. So many must remember that this element is vital for Google, Facebook, et al. to determine if your site is relevant. 

* When people stay at your site and engage, you win. If they leave in 30 seconds or less, keep improving because this hurts your reputation and business.

5. Build Your Community:

* Foster a loyal community around the brand on platforms like Discord and Clubhouse or through webinars and live events.

Key Takeaways

With changing search result layouts and the growth of AI, businesses need to show up in a few places.

* Prioritize diversifying traffic sources, including search, social, communities, email, and paid ads, and create joint ventures with others looking to grow their business.

* A Podcast is excellent for this because, for a short talk, you share interesting content with your audience, and the guest shares it with their audience.

* Optimize video, stay updated with SEO trends, and enhance the overall user experience at your website, social media, and wherever they go to learn about you. 

The shift isn’t just about survival but finding new, innovative ways to thrive.

Infuse your content with the power of your customer’s intention, creating based on what is relevant to them and not what you make up alone.

What is AI-powered search doing?

It’s a bridge from old search guesswork into a new world, connecting you to why customers do what they do and building that understanding into your content.

Take these steps, and like this podcast, and with patience, you’ll see.

90% of the game is showing up with human-generated content!

RESOURCES

SEARCH

* Adam Riemer’s Blog: One of the best I know, with great advice and mad skills.

* AI vs Human Written Metadata: Which Leads to Higher CTRs?

From Seer Interactive, excellent resources and great updates they share.

TOOLS

* Frase AI Search

* Surfer SEO

* Google Search Labs: Free way to see what they are up to

PLAGIARISM CHECKS

* Originality.ai

* Grammarly

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Remember how easy creative life was just 2 years ago. Creators wrote books, paintings, and pictures; curators asked permission to use parts in their work, with payments if sales were made.

Out with the Old, In with the GenAI: The Transformation of Copyright

A creator and a curator shape how others experience the world.

A creator by producing new works of art, literature, music, etc., and a curator by prompting and presenting works of art, literature, music, etc., for an audience using GenAI.

The world of copyright laws looks like a grand old library. Each book is a masterpiece hiding behind glass cabinets with stern lawyers metering out usage.

GenAI looks more like a shape-shifting librarian who opens the books while whipping up new chapters, alternative endings, and entirely new novels!

Is copyright law a Luddite position in the age of AI?

What is creative content - should it be copyrighted - and how do we keep original works going so AI creates better results instead of models collapsing into mediocrity and stagnation?

Old Rules, New Tools: How GenAI is Rewriting the Copyright Handbook

Copyright law is irrelevant.

“Copyright law is not just antiquated but soon irrelevant. If we get to the point with unlimited real-time content creation, there is no need to steal anything, or try to hold on to the finite amount of contact in libraries….

Copyright is sold to us to protect the artist. I hold that it’s a way to package art for sale to a mass audience and not needed.” 

Warren Whitlock

Pros

* Ingenuity Boost: Getting rid of copyrights leads to an explosion of creativity. Removing legal barriers allows for rapid mixing, change, and distribution of existing content.

* Ease of use: A lack of copyrights makes content accessible to everyone. This democratizes knowledge and creative works, providing more opportunities while removing limits.

* Less Lawyers’ Fees, More Creative Payments: Legal disputes for copyright infringement disappear. All save costs from legal advice, litigation, and copyright registration.

* Rapid Iteration: New versions of a creative work appear rapidly, pushing quality through collective effort.

* AI Enhancement: AI-generated work will not replace human creativity but enhance it.

Cons

* Loss of Income: Copyright law provides a revenue stream for creators. Monetization is undermined, though only a few people currently monetize their creations.

* Commodity-driven content erodes AI: It may focus creators only on what they can sell instead of inspiring originality, eventually undermining AI quality.

* Quality Concerns: Copyrights are an incentive to invest time into creating high-quality works. Without incentives, the quality of content may decrease. Why bother?

* Groupthink undermines creative ventures: Anyone can blend anything into their work without copyrights. Individual creativity may need to be noticed.

GenAI and the Future of Creative Ownership: Are Artists Being Written Out?

The Copyright Protects My Original Work

“AI stole from artists in the first place. In some pieces, you can even see an original artist’s signature incorporated into the work. AI isn’t creating new work, it’s taking pieces of existing works and putting them together....

I don’t think AI is a bad thing. I do think that the way it’s been built is. Some of my work was on sites that have belatedly announced that they allowed all artworks to be scraped by AI bots.

For some of mine, I probably would have given permission, if asked. For others, I would have denied it, but I never had the chance to make that choice.

Some sites do say they’re training AI with your creative works but hide it in their settings. Adobe does this. You have to go into settings and change your permissions if you don’t want your work scraped.”

Te-ge Watts Bramhall

“The success and profitability of OpenAI are predicated on mass copyright infringement without a word of permission from or a nickel of compensation to copyright owners.”

 Franzen, Grisham and Other Prominent Authors Sue OpenAI

Pro Copyright: Preservation of Human Ingenuity and Ownership

* Incentive for Originality: Copyright rewards original creators with exclusive rights. This motivates artists to invest time and effort, resulting in high-quality creations.

* Quality Over Quantity: With copyright security, artists focus on quality instead of mass-producing content. Exclusivity ensures an economic incentive for intricate, thoughtful work.

* Protection Against Model Collapse: In a system that relies solely on AI for creative output, there’s a risk of “model collapse,” where AI-generated works degrade in quality due to the loss of original human input. Copyrights ensure a continuous supply of new, high-quality, human-created content for AI systems to learn from.

Cons of Pro Copyright

* Access Inequality: Copyrighted material is expensive or difficult to access, stifling the spread of knowledge and creativity.

* Limiting Inspiration: Copyright restrictions block the remixing or adaptation of existing works, limiting new creations built on previous ones.

* Old Laws, New Rules: Copyright law is outdated for the new era of GenAI. Old laws will limit access to AI content while paying copyright owners is challenging. 

A 2-tier system will clearly develop, with high-quality content owned by major companies like Getty Images licensed at a higher pricing tier than Public Domain content, available to everyone.

Unasked and Uncredited: 

The Ethical Dilemmas of AI Scraping Content without Permission

Much of the content scraped for AI was taken from social media, sites like Wikipedia and Reddit, some websites, scientific articles, and collections gathered with permission.

Much was collected without asking; you can see in images that sometimes an artist’s signature will appear.

* What do we do with content that didn’t get permission?

* Ownership – who owns what now? Is there any way to tell what content is in the AI black box?

* Reset – if copyright lawsuits are successful, will the LLMs have to start from the beginning? 

This would be a massive blow to current AI, and I’m not sure it’s feasible.

Using Creative Content for Training Data without permission

AI training is based on existing creative content, sometimes without consent. 

It’s like a chef using someone else’s secret recipe to create a new dish and claiming it as original. This blurs the boundaries of ownership and ethics.

Permission – will AI be made opt-in, expressing authorization, rather than opt-out, removing consent?

Transparency and Choice

Clear communication is needed from platforms like Reddit or Wikipedia, allowing AI scraping. Even if permissions are hidden within settings, as with Adobe, the duty to opt out is unfairly placed on the creator.

It’s like being automatically added to a digital library you didn’t choose, with the burden on you to drop out.

Historical Digital Theft and the New AI Game

The Internet includes a longstanding struggle against unauthorized use of creative works. 

The evolution of AI adds a new layer to this challenge. It’s like having a neighbor steal from you, only to find that a more sophisticated thief is quietly stealing without you knowing.

Legal Threats

Ongoing lawsuits, if successful, could wipe out all AI-trained data violating copyrights. This could serve as a reset button, establishing new ground rules for AI training.

Solutions and Next Steps

* Retroactive Permissions and Royalties: It’s possible to seek retroactive permissions and possibly royalties for those whose work was used without consent. Not likely and maybe impossible.

* Opt-in Models: Platforms adopt a transparent, opt-in model for content creators willing to use their work for AI training. For example, OpenAI has code that can block its bot from accessing the content on your website. However, social media is still wide open and hard to protect.

* Legal Precedence: The lawsuits could set important precedents. If AI companies are found to be in violation of copyright laws, this forces a rethinking of how AI training is done.

AI Divides into 2 Tiers – 

High-Quality Copyright Content and Public Domain/Creative Commons

While AI is changing the world, it can’t shift copyright ownership and rights. 

The idea that anyone can go into a free or low-cost tool and access IP like Star Wars characters is both promising and threatening to companies who thrive off paid access to that content.

There are new worlds of AI content developing, one expensive and one based on what we call GenAI:

* Expensive and high-quality paid content – like Star Wars or Getty Images, for example – with a licensing fee and attribution to pay for access.

* Digital watermarks are applied to content with fees based on the percentage used, access, recurring payments, etc. It will be an ongoing negotiation at the most significant levels of content ownership, like movies and major media.

* Creative Commons Licensing: Offer all creators the right to apply a Creative Commons license, which protects their work, provides attribution when used, and pays where appropriate.

* Current public domain and permission-based content within GenAI is affordable for the rest of us. This includes attribution for works if people apply and place the watermarks in for some compensation and control, though limited to an amount that reaches a minimum for payment.

Consider adding an incentive for free access to GenAI for creators at a reduced or freemium in exchange for creating original content.

What does this new world look like to you? How should it work?

1. Creator Copyrights: Traditional Copyright for Human-Generated Original Content

The existing model for human-authored works without AI involvement is covered by copyright law. Creators hold exclusive rights to their works, including distributing, replicating, and monetizing. It’s like having private content where only the original owner has the key.

How it Works:

* Registration with a copyright office.

* Artists have the sole authority to license or sell their works.

* Infringements are punishable under existing copyright laws.

2. Curator Copyrights: Co-Creation Copyright between GenAI and content curators.

The Curator Copyright addresses both human artists and AI as co-creators. 

Credit and royalties are split between the human curator and the AI’s developers or owners.

The definition of a curator would involve prompts and other activities people enter into GenAI to co-create content. A curator builds the framework and ideas via prompts, and GenAI creates the content.

How it Works:

* A new legal framework defines “co-creator” rights concerning AI.

* The work carries a dual attribution, like a scientific paper with multiple authors.

* Royalties are divided based on a pre-agreed percentage between the human curator and the AI’s developers or owners.

3. Smart Contracts for Monetizing GenAI Content – Beyond Copyrights

Smart contracts automatically enforce rules around the use and monetization of AI-generated content. These contracts could be built on blockchain technology, making the agreements transparent and self-executing.

How it Works:

* The smart contract specifies conditions under which the AI-generated content can be used, modified, or monetized.

* If there is no smart contract, as in most cases, the content is in the Public Domain.

* When a user interacts with the content outlined by the contract, a predefined set of actions and/or payments automatically occur. For instance, a small fee is paid directly to the AI’s owner.

* Unlike traditional contracts, these are self-executing and don’t require agents.

Key Takeaways

Different types of creations require other legal frameworks:

* Creator Copyright is for purely human-generated works, offering complete control.

* Curator Copyright recognizes human and AI contributions, sharing credit and potential revenue.

* Smart Contracts allow for dynamic and automated monetization of AI-generated content outside copyrights.

Each model has its unique benefits and challenges but offers a pathway for preserving human creativity and accommodating the role of AI in content creation.

The Case for Copyrighting AI-Generated Art and Content

AI doesn’t operate alone, despite collaging together ideas from various sources. AI’s capabilities are initiated, guided, and fine-tuned by a human, giving specific direction to the creative process.  

Without humans, there would be no AI-generated content. Without AI, we couldn’t quickly enter prompts and create content.

This interdependency is vital, especially since AI benefits from original ideas and content. It doesn’t create anything original.

 If people just used AI and stopped creating unique works, what’s called a “model collapse” would happen, as the quality of AI decreases if it relies only on its own input and not on human ingenuity.

The Idea of Co-Pilots in Art Creation

As co-pilots, AI and humans are a powerful combination with unique skills—humans share the creative vision, and AI brings this to life with computational power.

We learn from the AI’s outputs, refining creative instincts while AI adjusts algorithms based on human feedback. It’s a symbiotic relationship evolving over time.

Five Ways to Adapt Legal Rules

* Expand Legal Rules Beyond Human Authorship: The legal definition of ‘author’ could include humans and AIs as co-creators entitled to joint copyright ownership.

* New Norms for Originality: Establish new measures of originality in AI-generated content. Evaluate the extent and quality of human prompts in guiding the AI.

* Copyright length depends on the act of creating: Introduce different durations of copyright protection based on the extent of human-AI collaboration. There is a difference between human-created content and that done in collaboration with AI.

* Digital Credit: Develop fixed digital signatures proving the ownership of AI-generated art, including the level of human involvement.

* International Copyrights: Make global copyright rules because AI is international.

Measuring and Validating Originality in AI-Human Collaborative Art

Evaluate originality by measuring the influence of people and AI in content creation. 

For example, consider the extent of the AI’s algorithmic difference from its training data influenced by human involvement.

AI content experts evaluate the uniqueness and creativity of the work, focusing on the interpretative choices made by the human creator.

Creating art at the intersection of human imagination and artificial intelligence deserves its own category of legal protection.

Explainability and Interpretability of AI

Explainability and interpretability of AI systems become crucial when measuring and validating originality.

When an AI system’s decisions can be traced and when we can comprehend how it made creative choices, it strengthens the measures for originality.

For instance, understanding the AI’s “attention focus” or the features the model deems relevant allows a view into how the human input influences those choices.

Given its training data and the human-guided alterations, it provides a framework to gauge the uniqueness of the AI’s output.

Quantifying Human Input

How much a person prompts, edits, and adapts a work may give a quantifiable measure. A session involving 100 prompts may show a more profound human enhancement than one of 10 prompts.

However, the focus should also be on the quality of the input. For example, a single, insightful prompt taking AI’s creativity in a unique direction could be more indicative than 100 trivial updates.

The Curator Copyright for Joint Creation

The development of AI copyright laws must be carefully considered to balance the interests of creators, users, and the tech industry.

If curator copyright for AI-human joint content creation is created, there are several ways to make it work for both sides:

* Revenue Sharing: The AI company and humans enter revenue-sharing agreements for any income generated.

* License Fees: The AI company charges a license fee for generating copyrighted material. This could be a one-time or recurring charge based on sales activity.

* Rule Setting: The AI company sets guidelines for joint copyright. For example, specify the minimum or maximum prompts required or levels of customization outside of AI (like editing in Photoshop or rewriting an article).

* Partial AI Rights: The AI company retains partial rights to the artwork and shares in sales, reproductions, or variations.

* Smart Contracts: On blockchain networks, automatically distribute royalties between humans and AI companies based on agreed-upon rules.

Can AI and human artists co-author the next chapter of creativity, or will they be in a courtroom drama? 

Stealing content has been a problem for years on the Internet – is AI doing the same thing, or is this new?

Few Ideas to Keep Us Creating and AI Growing

* Blockchain for Credit – Like Creative Commons but automatic: Instead of copyrights, a blockchain ledger could keep track of who first created a piece of work. Although the result could be freely used and modified, credit is given to the original creator.

* Freemium Model: Creators offer free basic versions of their work while charging for premium versions. This could maintain economic incentives without stifling creativity.

* AI as a Collaborative Tool: Instead of replacing human artists, GenAI serves as a tool for enhancing human creativity.

Suddenly, the old game of swiping content goes high-tech, and this new player turns heads and raises eyebrows.

While GenAI is busy creating its own Mona Lisa or penning sequels to “Oppenheimer,” it’s doing so by sampling from countless artists without asking for permission.

Do the original creators—the artists, writers, and thinkers creating the content— own the rights to their work in this AI-driven future?

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Right now, I can take any art, photo, or writing you create with AI and take it, sell it, and do what I want. Because if it came out with the help of AI, you can’t copyright it.

Want to understand why AI copyrights don’t happen?

Award Winning GenAI Art in 624 Prompts = The Void of Ownership

What Rights Does Copyright Give?

Is AI-Generated Content “Fair Use”?

Challenges to the Inability to Copyright AI-Generated Content (and a ray of hope for copyright protection)

It’s like a digital artist, a copyright lawyer, and an art buyer walk into the artist’s gallery…..

The art buyer asks the artist, how much for that AI art you created?

Nothing says the lawyer – it’s AI-generated, so it’s yours for free.

Both digital artists and AI copyright are challenges to traditional notions of artistic creation and ownership.

Digital artists break traditions with their ability to create art unbound by the physical limitations of traditional media. 

AI copyright disrupts by developing new forms of intellectual property that do not fit neatly within existing legal frameworks.

And this isn’t a joke; it’s happening now and challenging the notion of copyrights, creativity, and how humans and machines collaborate in AI.

We’ll explore 3 cases of copyrights not given to AI-generated content, what rights you have lost by doing this, and the challenges faced if you do this or use AI content for clients.

Award Winning GenAI Art in 624 Prompts = The Void of Ownership

See that beautiful painting above? I share it without permission; I could sell it to you despite Jason Allen spending time, 624 prompts, extensive Photoshop editing, AND winning an award.

This piece, created with Midjourney, Théåtre D’opéra Spatial, won the Colorado State Fair award in 2022. Stunning work and no one owns it.

The creator, Jason Allen, went to copyright it after the Fair. The Copyright Office turned him down because humans didn’t primarily create that art. He would not disclaim the importance of AI-generated material in his work, claiming:

 “the office is placing a value judgment on the utility of various tools.”

While well intended, it needs to be a solid legal argument.

Courts don’t judge tools; they apply existing copyright law. In this case, the author is not human; AI is a machine. No copyright!

Copyright focuses on “expressive elements,” and much of this came from the AI working on Jason’s prompts.

No copyright because it’s not human-generated!

Takeaways – AI Content is Public Domain, meaning no one owns it…..yet.

Despite all his work, the prompting means he’s asking the machine to create the art. Even if he prompts for hours and edits it in Photoshop, the result wouldn’t happen without AI.

AI is not a tool; it’s a co-creator, and the best-case scenario might have been Midjourney, the technology, being granted the copyright.

The Copyright Mistake – Human Authorship is the foundation of the law.

“The Board finds that the Work contains more than a de minimis amount of content generated by artificial intelligence (“AI”), and this content must therefore be disclaimed in an application for registration.

Because Mr. Allen is unwilling to disclaim the AI-generated material, the Work cannot be registered as submitted,” the office wrote in its decision.

“In this case, “disclaim” refers to the act of formally renouncing or giving up any claim to the ownership or authorship of the AI-generated content in the work. The office is saying that because the work contains a non-negligible (“more than a de minimis”) amount of content generated by AI, Allen must formally acknowledge that the AI-generated content is not his own creation when applying for registration. 

As established by Copyright Office precedent and judicial review, US copyright registration for a work requires human authorship.”

US rejects AI copyright for famous state fair-winning Midjourney art – Ars Technica

What Rights Does Copyright Give?

Copyright law varies by territory, but in general, copyright gives the owner exclusive rights to:

* Reproduce the work: Make copies of the original work.

* Create derivative works: Modify the actual work to create something new.

* Distribute the work: Share or sell copies of the work.

* Publicly display the work: Show the work in public.

* Publicly perform the work: Perform the work in public, for example, a play or a song.

* License the work: Permit others to do any of the above with your permission.

Without a copyright, there is no ownership, and rights are vague. Even by publicly displaying this art, Allen broke the Colorado Fair rules.

He can’t reproduce, sell, modify, or license it. And he can’t stop anyone else from doing it, as this work is in the public domain, meaning anyone can use it (OK, maybe not sell it).

And if he had, as the Copyright Office said, acknowledged that the AI-generated content is not his creation, he’d lose the case anyway.

AI-generated art titled “A Recent Entrance to Paradise.” The image cannot be copyrighted, a judge ruled.

My Software Created The Art – Grant It the Copyright, and It will give it to Me.

Stephen Thaler has an unusual approach to copyrighting his work, ‘A Recent Entrance to Paradise”, which also did not receive a copyright, lacking human authorship.

Thaler reapplied for a copyright given to the software that created the art. Judge Howell states that Thaler tries:

“to complicate the issues presented by devoting a substantial portion of his briefing to the viability of various legal theories under which a copyright in the computer’s work would transfer to him, as the computer’s owner; for example, by operation of common law property principles or the work-for-hire doctrine.” United States District Court for the District of Columbia ruling

Thaler lost because he argued about who gets the copyright, not why it should be granted without a human author. Once again, no human authorship, no copyright.

For centuries, authorship in copyright has meant human authorship. The old and current copyright rules won’t protect anything not primarily created by a human being. Proving AI-generated content relies on a human being, as the author, is almost impossible.

Is AI-Generated Content “Fair Use”?

Now, there is one possible angle that OpenAI is trying to use in its many lawsuits, as well as other companies, and that is Fair Use:

John Grisham, George R.R. Martin Among 17 Major Authors Now Suing OpenAI

Getty Images Sues Stability AI Over Photos Used to Train Stable Diffusion Image Generator

Fair use usually refers to works of journalistic or satirical nature. Open AI is now trying to extend this definition to AI. 

It’s a problematic fit here as AI gathers many sources to deliver a predictable result, and not one is relied on, so the nature of the work is hard to define.

The key is whether it impacts the copyright owner. As you see above, famous authors and intellectual property owners are flocking to defend themselves against this onslaught of AI on their rights.

Does the artificial intelligence (AI) training process infringe copyright in other works?

“Copyright infringement occurs when one party uses another party’s copyrighted work without permission. Infringement, which takes place both online and offline, includes copying, selling, sharing, publicly performing, displaying, and/or creating derivative works based on a copyrighted work.

A derivative work arises when a body of work is created based on a pre-existing, copyrighted work. Federal courts interpret derivative works differently in part due to various interpretations of the fair use doctrine, which allows for use of copyrighted material without the owner’s permission under certain conditions. Under the fair use doctrine, it is permissible to use portions of copyrighted work for purposes such as news reporting, commentary, and scholarly reports.

Courts determine fair use by the following four factors:

(1) purpose and character of the use,

(2) nature of the copyrighted work,

(3) amount or substantiality of the portion used, and

(4) the economic impact of the use on the copyright owner.: “

Artificial Intelligence, Bensch Law

Anyone creating AI content faces challenges to their actual work and the impact of their work.

Agencies and service providers who use AI content must disclose this fact to their Clients, meaning that their clients do not gain the copyright to work they paid for.

Work-for-Hire and Copyright

In a work-for-hire scenario, the client expects to own the copyright of the work produced. The agency and the client formalize this through a contract. There are several problems:

* Copyright Transfer: If the agency uses human creators, the copyright transfers as usual. However, if the content is AI-generated without copyright, the transfer is not possible.

* Liability: Who is responsible for any legal issues if the content is AI-generated and not copyrightable?

* Client Expectations: Clients expecting to receive copyright ownership as part of the agreement won’t get it due to the nature of AI-generated content.

* Licensing Issues: If the agency uses the content for multiple clients, the lack of copyright makes this legally questionable.

* Future Use: If the client doesn’t own the copyright to the AI-generated content, they are limited to future use, such as modification, distribution, and public sharing.

With the wave of companies using AI, even within their companies, how many are reviewing whether it’s legal, leaves them liable, and opens privacy concerns?

Even Amazon requires authors to disclose whether books published are “AI-generated” or “AI-assisted.” 

AI-generated means the creation starts and is delivered with AI, while assisted doesn’t have to be revealed, meaning a human created it and then uses AI to help edit and finish it.

Challenges to the Inability to Copyright AI-Generated Content

* No Legal Protection or Attribution: Anyone can use AI-generated content without permission, maybe to sell or license. Who’s to say who owns or created the content?

* Monetization: If you don’t own the copyright, how can you monetize it legally?

* Intellectual Property Right: Without a copyright, there’s no protection for the ideas or creative content.

* Plagiarism: While plagiarism is an ethical issue, how can you prove you created it in the first place?

* Legal Ambiguity: The legal framework around AI-generated content is still developing, so there are no specific rights today.

* No Licensing: If you don’t own it, you can’t license it.

* Loss of Control: you can’t control how it’s used or shared.

* Ethical Problems: if someone uses your content in an unethical manner or in a context you disapprove of, you don’t have any way to stop them without a copyright.

Zarya of the Dawn – a Ray of Copyright Hope

Kristina Kashtanova’s comic book, Zarya of the Dawn, does show some promise for copyright law. 

The Copyright Office rejected the graphic images created for the book with Midjourney, but the actual publication text, organization of images and text, and the document did receive a copyright:

“We conclude that Ms. Kashtanova is the author of the Work’s text as well as the selection, coordination, and arrangement of the Work’s written and visual elements.

That authorship is protected by copyright. However, as discussed below, the images in the Work that were generated by the Midjourney technology are not the product of human authorship.

Because the current registration for the Work does not disclaim its Midjourney-generated content, we intend to cancel the original certificate issued to Ms. Kashtanova and issue a new one covering only the expressive material that she created.” Zarya of the Dawn Letter, Ars Technica

We’re cruising through the 21st century, but our copyright laws are still jamming to the legal records of the past.

This lack of legal clarity throws a wrench into the gears of creativity!

Imagine pouring your heart and soul into a masterpiece, only to find you can’t protect it because the law doesn’t understand your robotic co-pilot.

The cutting-edge algorithms of AI leaders like OpenAI, Microsoft, and Google could drown in a sea of lawsuits. Instead of a new age of art and content, we might follow old rules that don’t understand the power and possibilities of AI.

AI copyright is like a cloud of smoke, challenging to contain and impossible to control. 

Until the law catches up to recognizing more than human authorship, it’s a bit like painting on a canvas that erases itself.

Without a copyright, anyone can take your content, call it their own, and sell it. Defining how to copyright AI content and what’s possible is in our next pod.

 Until then, step slowly and understand that you don’t own what you create with AI….yet.  

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

💡Bias is a trickster of truth and the source of so many challenges in AI.

Falling in love with ChatGPT, I search for sources about “flow cytometry” to share and impress some bioscientists I work with who haven’t yet joined my AI rapture.

As the answer comes in before I call, I check the sources. Everyone fake. Not one truth. The scientists could have been more impressed. Snap me out of my AI rapture!

Humans data school AI systems and humans are flawed. That’s why focusing on AI fairness and ethics isn’t about philosophy. It’s about the practical impact of AI on society, business, and you.

Today, the AI community embraces the challenge of creative and effective ways to develop unbiased AI. Though biases, known and unknown, influence the content foundations of data.

Let’s dive into the most promising practices, making AI better, fairer, and more in tune with the diverse world they serve.

Begin with Diverse Data Collection

4 Solutions to Removing AI Bias

Key Elements to Remove Bias and Create Better AI

The Basic Need – Business Example to Give AI Diverse and Typical Training Data

The first way to tackle bias is like cultivating a well-balanced garden: diversity is vital. Why grow tomatoes when you can plant a variety of lettuces, herbs, and fruits that appeal to more people?

Imagine training an AI to understand business customer sentiment only using data from a single demographic. You’d be missing out on the wide variety of customers’ experiences.

Instead of training your AI on a narrow dataset representing just a sliver of your customer base, aim for a broader, inclusive range.

A diverse dataset translates to an AI understanding and responding to a broader range of human experiences. That’s good for customers and great for business.

Getting this balance isn’t easy. It requires attention to data training and tweaking so you’re not unwittingly introducing bias into your model.

Bias in business can lead to inaccuracies and legal problems – remember the lawyers who trusted ChatGPT with their submitted brief, only to find the sources it shared didn’t exist?

They lost their right to practice law because they didn’t check the facts. That’s what bias can do to your results.

AI leaders are exploring solutions focusing on fairness ethics and ensuring we understand how and why AI comes up with the answers.

Begin with Diverse Data Collection

* How: Make sure the data you’re using represents different kinds of people and viewpoints.

* Example: for a facial recognition system, include faces from various ethnic backgrounds, ages, and genders.

Carefully select the data used to train the AI model so it represents a wide range of scenarios, views, and people.

Be sure to include data from underrepresented groups and adjust to remove skews toward dominating groups. 

Make the training data as inclusive as possible to make the AI model unbiased. Garbage in, garbage out is what causes AI bias and poor results.

Advantages:

* Wider Audience: A model trained on diverse data applies to more people.

* Fairness: Make sure that all groups are represented.

* Ethical: Diversity tends to lead to moral outcomes.

Disadvantages:

* Data scarcity: For some groups, diverse and large-scale data might not be available.

* Imbalance: It’s not easy to achieve balanced data, especially with embedded societal bias.

* The subtlety of Bias: Bias can be tricky, embedded in data, and often in people evaluating it.

The Simple Method to Remove Bias is Not Good

A few AI models tried ignoring variables like race, gender, or age when training to improve. 

It’s like saying, “Let’s not see color; let’s treat everyone equally.” While this sounds fair, it doesn’t work because bias isn’t a category. It’s a fact of life in language and actions.

This approach ironically extends the biases we are trying to eliminate.

You risk ignoring systemic issues that do affect people differently. That’s not just bad ethics—it’s also bad business.

Taking the time to develop a balanced and diverse dataset is smart. It’s about accepting that our world has different and equally important perspectives. The challenge is not just technical; it’s a business strategy and an ethical commitment.

By treating it as such, you create an AI tool more in tune with the complex world it serves.

4 Solutions to Removing AI Bias

1. Fairness-aware Frameworks

Creating AI models for fairness to check and balance AI training is becoming a standard practice at Open AI, Meta, and other companies. Creating a set of rules that provide an almost human sense of right and wrong, what’s fair and what’s not, with a sense of compassion:

“they will have emotions, they will have empathy,

they will be all the things we require entities in the world to be if we want them to behave properly.”

Yann LeCun, Meta VP & Chief AI Scientist from the

Munk Debate on Artificial Intelligence - see video below

Setting specific fairness objectives optimizes for both predictive accuracy and fairness. The result is a model designed to ease bias in its predictions.

Fairness models are complex, expensive to train, and require customization to meet fairness goals.

* Preprocessing Data for Fairness

Preprocessing involves curating the data before feeding it into the algorithm. By identifying and addressing biases in the data, the resulting balanced dataset helps generate fair outcomes.

However, this method requires a deep understanding of the potential biases and often assumes that unbiased data is available, which is only sometimes valid.

* Ensemble Learning: Combining Multiple Outputs

Ensemble methods involve training multiple models and combining their outputs for a more balanced and accurate prediction.

* Pros

* Balancing out individual model biases.

* Often improves overall model performance.

* Cons

* Computationally expensive.

* Adds complexity in model management and interpretation.

Examples & Possible Uses

In a language translation service, ensemble learning can aggregate output focusing on different dialects or styles, offering a nuanced translation and reducing bias towards any particular form of the language.

* Adversarial Training for Fairness

Adversarial training leverages oppositional networks to detect and counteract biases. 

One network tries to make fair predictions, while an adversary tries to find biases. The primary model continuously adjusts to counter the adversary. 

Once again, the cost of fairness is a computationally intensive and complex process.

* Transfer Learning for Fairness

Transfer learning involves models pre-trained on a large, diverse dataset. These models are then fine-tuned for applications.  

Existing biases in the pre-trained models might transfer to the new application, and this approach is also limited to where fair pre-trained models are available.

2. Reviewing AI - Audits, Analysis, & Explainability

Explainability: Unlocking the “Black Box”

Explainability seeks to make the decision-making process transparent. It identifies which features the model gives weight to, helping debug and modify the model.

* Pros

* It provides a lens to inspect and correct model biases.

* Useful for compliance with fairness criteria and regulations.

* Cons

* Achieving explainability in complex models is challenging.

* It may compromise model performance for the sake of transparency.

Content Recommendation Example

In content recommendation algorithms, explainability ensures the model doesn’t prefer content based on controversial or sensitive features like political affiliation or gender. 

* Post-hoc Analysis and Adjustments

These techniques analyze a model’s decisions to identify and correct bias. They adjust the model’s outcomes rather than the model or training data. 

The benefits are quick adaptations to already deployed models. But this approach solves the problem after the fact and might introduce other errors.

* Audit Algorithms

Audit algorithms inspect model outcomes for signs of bias. Audits run in real time, flagging biased predictions for manual review or automated adjustment. 

Excellent for monitoring, it does not remove the underlying bias and often requires manual adjustments, which takes more time.

3. Interpretability: Navigating the Complex Landscape of Bias

AI models make decisions based on patterns and relationships in massive amounts of data. 

We’re often unsure how the outcomes happen, making AI decisions harder to interpret and assess for fairness.

Researchers are actively exploring attention mapping and rule extraction methods to show how these models arrive at their outputs.

Rule extraction is a technique to understand the underlying decision-making process by estimating its behavior with human-readable rules. 

The idea is to take a trained, complex model that performs well on a particular task and guess its behavior using a more straightforward, rule-based model.

Here are some standard techniques for rule extraction:

* Decision Trees and Random Forests: In decision tree-based models like Random Forests, the rules are directly extracted from the paths in the trees.

* Sensitivity Analysis: By studying how small changes in input features affect the output, creating rules capturing the model’s decision boundaries.

* Neural Network Rule Extraction: Specialized techniques for extracting rules from neural networks, like breaking down the network into a more interpretable structure and translating that structure into rules.

Benefits of Rule Extraction:

* Interpretability: The extracted rules make understanding why a model makes a specific prediction easier.

* Debugging: Understanding the rules helps identify biases or errors.

* Compliance: In some industries, interpretability is a legal requirement.

Rule extraction is not always perfect and may result in a loss of accuracy since the simpler model approximates the more complex one.

Attention mapping is a way to visualize and interpret the model’s decisions. 

The attention mechanism within these models weighs the importance of different parts of the input when generating output. Useful in language translation, question-answering, summarization, and other tasks.

Example: Translating the sentence “The cat sat on the mat,” an attention map might show that the word “sat” is strongly associated with “cat” and “mat.” The model pays attention to these relationships to understand the context.

Attention maps are helpful for several purposes:

* Debugging: Understanding where the model is paying attention helps diagnose issues in the model.

* Interpretability: Attention maps provide insight into the decision-making process.

* Feature Importance: Attention maps help understand what features (words, tokens, etc.) are essential for a particular task.

Attention is just one part of a model’s architecture, and focusing solely on it might not capture all the degrees of the model’s behavior.

4. Ethics and Bias-Free AI:

The Quest for Ethical Algos and Fair Outcomes

Can I line up the jobs to be done, the safe way to do them with automated governance and compliance with the laws that are emerging, to get the job done?

Ramsay Brown CEO, Mission Control

We want AI to understand and respect all kinds of people. By doing this, individuals will trust AI more, whether buying something online or getting medical advice.

* Think of an ethical guide as a checklist for building AI that does good, not harm. Helping everyone on a team know what to focus on, from the people who collect data to those who write the code.

When we build AI the right way, it’s not just about avoiding mistakes. We’re creating technology that makes life better.

Ethical AI fits anywhere—it works well in different countries and respects people’s diverse views and needs.

Ethical frameworks in AI aim to create algorithms that interact fairly, impartially, and reflect diverse viewpoints.

The framework outlines the principles guiding data collection and model training. AI then becomes an efficient tool serving the values and norms of society.

Infusing ethics into AI is a hands-on job and involves multiple steps:

Ethical Guidelines

* How: Create a set of rules or principles that your team agrees to follow.

* Example: Before starting a project, agree that they will not use AI for harmful purposes like discrimination or surveillance.

Bias Audits

* How: Check AI models to see if they’re making biased decisions.

* Example: For AI sorting job applications, make sure it’s not favoring certain groups.

Transparency

* How: Make it clear how the AI is making decisions.

* Example: If AI is recommending loan approvals, show how the AI came to its conclusion.

Ethical Review

* How: A team of diverse experts review and approve AI projects.

* Example: Before launching a health diagnostic AI, review it by a panel including medical experts, ethicists, and patients.

Privacy Protections

* How: Build features that protect user data and privacy.

* Example: When creating a voice-activated assistant, allow users to delete their conversation history.

By actively integrating ethical considerations into each step of AI development, we’re more likely to create intelligent and fair systems.

Key Elements to Remove Bias and Create Better AI

At this point, cynical minds arise, saying this sounds good, but can we do it?

Remember, we’re the teachers here; AI is learning from us. The responsibility to ensure it’s fair and unbiased is up to us. Technology serves humanity if we set the rules and follow ethical guidelines.

And the best part? We’ve got the tools to do it. We’ve discussed some promising steps already scrubbing out those biases.

These aren’t pie-in-the-sky ideas; they’re practical, actionable ways to guide our tech to serve all of us, not just some of us.

We can shape the AI world into a more ethical and inclusive place.

Because the fear of AI, at its heart, is the fear of what people would do with this power. 

Humbled by the threat, we imagine a world where these threats don’t happen because we have rules to check the problems and provide accurate and helpful solutions.

AI Bias is a symptom of our problem, and by working on eliminating this bias, we get to know ourselves a little better and become aware that AI Bias is a reflection of our society.

One we can change with a bit of help from AI and international cooperation. It is possible.

The basics are simple:

* Accept responsibility as a society. The bias AI shows us is a mirror. Change the reflection.

* Diverse Training Data: Make sure training data represents different groups.

* Audits: Third-party audits help identify biases in AI.

* Fairness-aware Algorithms: Develop algorithms sensitive to fairness rules.

* Transparency and Interpretability: Making algorithms more understandable helps identify how biases form.

* Ethical Guidelines: Establish a framework for ethical AI leading to unbiased systems.

* Human-in-the-Loop: Involving humans in AI decisions adds a layer of valuable perspectives.

* Legal Frameworks: Hold organizations liable for biased outcomes.

* Diversity in AI Development: Diverse development teams help reduce biases.

* Global Standards: As AI becomes global, international fairness standards arise. Follow them.

Dive Deeper

* Turing Institute: Fairness, transparency, privacy

What can we do to ensure the decisions made by machines do not discriminate, are transparent, and preserve privacy?

* What Do We Do About the Biases in AI?

* Detecting and mitigating bias in natural language processing

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

The bias lies not just in AI but in ourselves. We create the content, select the data, try to set the rules, and help AI do seemingly magical things.

Bias also has different meanings worldwide and is a threat everywhere.

Understanding the bias in our societies helps us improve AI and remove harmful bias.

To avoid making the mistakes we did with early AI algorithms on social media, leading to hyperpolarization, creating deep echo chambers, and enticing younger generations into a new world of unfair comparison and social bullying.

But it's not too late, and we must read the signs.

We have an idea of what is right, what is truth, and what is wrong. Yet that is a subjective view, not a shared one, not even within countries or regions.

The AI Alignment Problem

8 Examples of AI Bias

Becoming Aware of Bias of AI Developers - Diversity and Multiple Perspectives Needed

Takeaways on Bia and the Weakness of AI

Belief in AI and the Placebo Effect: Let's Start Becoming the Solution

Seeing how pervasive AI bias is, what can we do to stop it? Our challenges go beyond AI, from cultural and power dynamics to creating a shared definition of ethics.

How we perceive and address bias, especially in personalized learning, is challenging to unravel these intricacies of bias in AI and ourselves. 

Let's explore the origins of bias in AI and the ethical dilemmas it presents.

Begin with the roots of bias and its impact on this wave of AI development.

Bias Definition

* A preference or an inclination, especially one that inhibits impartial judgment.

* An unfair act or policy stemming from prejudice.

From The American Heritage® Dictionary of the English Language, 5th Edition

The bias lies not just in AI but in ourselves. We create the content, select the data, try to set the rules, and help AI do seemingly magical things.

Bias also has different meanings worldwide and is a threat everywhere.

Understanding the bias in our societies helps us improve AI and remove harmful bias.

Cultural Context

Different societies have different norms and values. What is a harmful stereotype in one culture may not be in another. 

For example, how Americans and Russians view each other's countries is shaped by historical, political, and cultural contexts unique to each nation.

Power Dynamics

The impact of a stereotype depends on the power dynamics in play. 

Stereotypes prolonged by a majority group about a minority group create significant real-world. 

For example, a stereotype about a majority group may not have the same impact on their life chances, job opportunities, and social standing as it would for a minority group.

Ethical Frameworks

Varying ethical frameworks offer ways to evaluate the impact of stereotypes and biases. 

We might focus on the overall harm or benefit that a stereotype produces or concentrate on the act of stereotyping itself as morally wrong. What is truth and what is not varies between countries and individuals.

When AI goes wrong, we complain and hide in fear. Today, those impacts are essential and even more critical as AI grows.

For example, I asked Midjourney to create an image of AI Bias (disclosure: that was it for the prompt; I kept it super simple so I could see the results), and this is what I got:

An attractive white woman. While not the answer, it's a symptom of AI Bias.

It only sees the data we show it, and just now, as we wrap our heads around what this means and how to create a world where fairness, transparency, and explainable AI are the rule, not the exception… It's scary.

Both bias and truth are lenses through which we view the world—bias is a way of filtering out information that is inconsistent with our beliefs, and truth is a way of seeing the world as it is without distortion.

Both an echo chamber and bias can lead to a distorted view of reality—an echo chamber reinforces existing beliefs, opinions, and preferences by preventing us from considering new information or perspectives.

The AI Alignment Problem

AI alignment is the issue of how to create AI systems in a compatible way with human moral values.

"The alignment problem has two parts. The first is the technical aspect which focuses on how to formally encode values and principles into AI so that it does what it ought to do in a reliable manner.

Cases of unintended negative side effects and reward hacking can result if this is not done properly [2].

The second part of the alignment problem is normative, which asks what moral values or principles, if any, we should encode in AI. To this end, we present a framework to consider the question at four levels." 

A Multilevel Framework for the AI Alignment Problem - Betty Li Hou '22 & Brian Patrick Green Santa Clara University

The AI Optimist Debate Question #3:

 Your suggestion of embracing and filtering biases is innovative, yet it brings another question to light.

If we allow AI to learn and adapt to individual biases, isn't there a risk of creating echo chambers where people only hear what aligns with their pre-existing beliefs?

This could lead to a more divided society, as people become more entrenched in their viewpoints without being exposed to diverse perspectives.

How do we mitigate this risk while maintaining the personalized learning and business environments that AI facilitates?

Bias is a root and foundational element of AI. We must deal with the problem from within our society instead of trying to prevent its AI output.

Gamify it - we live in a world of bias.

AI "bias" involves systematic errors or prejudices in the model's outputs from the training data or the objectives it optimizes for.

Join us on a journey to understand the roots of bias and discover potential avenues for easing its impact in our ever-expanding artificial intelligence world.

AI Bias is a deep subject with many avenues. Start with understanding the problem of bias in AI, how it works, and where it comes from, and in our next pod, we'll explore what we are doing to stop this.

8 Examples of AI Bias

Examples of recent AI bias include:

* Data Bias: The AI model will likely reflect the bias if the training data has innate tendencies.

* Example: Amazon tried using AI for hiring and evaluating resumes. Amazon's machine learning tool influences hiring decisions biased against women, primarily men working for Amazon, creating historical data bias.

* Gender bias arises based on language, hobbies, and other factors skewing men from data. For example, men listed football as a hobby; women might enter softball.

* While this is obvious, the nature of how men and women communicate and the words they use are different. AI reads the difference and skews the results towards what had existed before, which was a predominantly male workforce for Amazon.

* Even when they tried to remove gender to balance this, the inherent bias developed by natural differences between men and women worsened the results. Ultimately, Amazon ended this initial test, understanding there were better solutions.

* Label Bias: Even if the data is diverse, the labels attached to that data might be biased.

* Example: Google Photos facial recognition system trained chiefly on images of white tagging dark-skinned people as "gorillas." Shut down immediately. This bias also arose for Flickr, which tagged white and black people as "apes."

* AI is not prejudiced; it reflects what data dominates.

* Algorithmic Bias: The math techniques used create bias, even if the data is unbiased.

Example: Social media algorithms create "echo chambers" that show content like what the user engages with, reinforcing existing beliefs or preferences.

4. Confirmation Bias: AI models reinforce the existing beliefs based on user activity.

* Example: News recommendation algorithms only suggest articles that align with the user's existing political beliefs.

* Interaction Bias: When the AI learns from its interactions and rules arising from these, it introduces new biases.

* Example: A hospital uses AI to determine who should immediately be taken care of in cases of pneumonia. Patients with a history of asthma are considered low-risk and suggested treatment as an outpatient.

* It turns out the AI misunderstood that asthma users were immediately sent to ICUs because their risk factors were higher.

* Because of this quick treatment, the AI found that asthmatics were less likely to die than others, creating the incorrect recommendation. It didn't know the cause, just the result, and optimized for that result.

* Optimizing for the likelihood of dying helped create this confusion, as the interaction was misunderstood and misapplied by the rules.

* Objective Function Bias: The objective function optimized becomes a source of bias.

* Example: COMPAS is an AI tool used in courtrooms to predict the probability of future criminal behavior and designed to make parole recommendations optimized for recidivism, focusing on previous arrests, age, and employment.

* Black defendants were wrongly labeled as "high-risk" to commit a future crime twice as often as white defendants.  

* These factors didn't include other social influences, developing a bias against groups with the objective function of higher recidivism in a system where black Americans are incarcerated 5x more than white people.

* Reinforcement Learning Bias: This rules model is based on rewards and punishments and is used for training AI to play games like Go and CoastRunners.

* Example: CoastRunners 7 test to win a race, the boat gets stuck in meaningless loops that maximize a simple reward at the expense of long-term goals.

* Boat-racing AI hacks the game by continuously collecting bonus items and crashing into other boats without trying to win the race.

CoastRunners 7 Example

* Shows that pursuing external rewards is different from how intelligence works. This bias concerns developers because the AI created the scenario, disregarding the havoc it created simply to gather points instead of achieving the objective of winning the race.

* While this is not a threatening situation, imagine creating AI Agents that do critical work that may impact humanity and human life. How do we know it will do the right thing, and how can we train it?

* Societal and Cultural Bias: Biases reflecting societal norms and inequalities.

* Example: word2vec by Google led what has become ChatGPT and others, a neural network that bunched words commonly associated with each other by "vectors ."Tokens in ChatGPT are often 2-4 words widely used together today.

* The problem with word2vec was seen later. If you typed China + river, you got the Yangtze. Typing Paris – France + Italy, you got Rome. Wow, this seems excellent!

* Then the foundational biases of language emerged: type doctor – man + woman gave the answer nurse. Type shopkeeper – man + woman, and the answer? Housewife!

* The bias of language impacted this early example, which has been addressed. Still, it points out the problem of societal prejudice because much of what we may not understand from AI comes from things we don't understand about our culture. The data source for AI is our society and our culture, so this is a constant issue to watch.

Understanding and mitigating these various types of biases is crucial for developing AI systems that are fair, equitable, and effective.

"Modeling the world as it is is one thing. But as soon as you begin using that model, you are changing the world, in ways large and small. There is a broad assumption underlying many machine-learning models that the model itself will not change the reality it's modeling. In almost all cases, this is false"

Brian Christian – The Alignment Problem

Becoming Aware of Bias of AI Developers - Diversity and Multiple Perspectives Needed

Reading from AI leaders about the future of AI is like reading the same philosophical trope - we must protect humanity. 

We must align with human values. We must control this uncontrollable thing that we don't know how it runs and regulate it the right way.

If one of them could show confidence, even OpenAI's CEO Sam Altman knows he doesn't know how this works. 

No one does, and while they keep trying to remove bias, it's like trying to eliminate what is omnipresent in society, hoping we'll have some Star Trek moment and suddenly realize we are all in this together.

The problem with AI isn't technical. It's us. We keep saying cooperation and collaboration. Meanwhile, tech creates monopolies and moats. 

Governments increase surveillance. Tech companies like Meta treat us as ad units driven by algorithms that cause us to isolate, polarize, and echo chambers.

In the early days of AI, the bias of these tech companies towards data-driven and model-driven solutions missed the most crucial element: human beings and humanity in general.

This approach is changing with a focus on fairness, transparency, and explainability (understanding what the AI is delivering).

Becoming aware of the biases that happened doesn't remove them, including:

* Self-regulation Bias: When a company self-monitors its legal, ethical, or safety standards. Initially, the rule that tech companies ask for is to regulate themselves. Instead of having a third-party entity monitor and enforce those standards.

If any company is asked to eliminate unethical behavior, often in the short term, they focus on eliminating the appearance of bad behavior rather than the behavior itself.

When Cambridge Analytica abused data on Facebook, it continued for months. Then Facebook kicked them off, but the data learning had fueled their algorithm. 

Scapegoating Cambridge Analytica misses that the algorithm benefits from the learning from now on.

* Attribution bias: Human beings love to make attributions about the causes of their own and others' behaviors; rarely do these reflect reality.

The example of COMPAS and the parole issues showed that the data scientists involved needed to understand the social impact. Sometimes, the technical point of view, trusting the data, doesn't show that the problem is the type of data involved, recidivism, and race.

Positive changes are happening by focusing on recidivism within specific age groups regardless of race, and awareness of the bias helps mitigate it.

Tell that to the many who went to jail because of their race. This example is why many fear AI because we often lack a deep understanding of social impacts.

Because this bias is hard to define until the damage is done, social scientists and some ethics professionals are helping improve the process.

The more we understand our own biases and those of our societies and cultures, the better job we are starting to do to address these issues. 

However, these are complex and difficult problems to overcome.

* Framing Bias: AI evolves with the quality of data and the quality of the training on that data. Framing often involves social constructs of truth and viewpoints by media sources, social media polarizing discussion, political movements, and other bountiful sources of bias.

AI is gathering data we have created. Today, we are trying to figure out how to remove the bias, but the tendency is coming from society.

AI only reflects the data that shows how people organize, perceive, and communicate about reality.

AI Developers are trying to stop the extreme stereotypes and prejudices within a black box of AI driven by advanced math.

The AI industry is listening. 

OpenAI is trying to align with human values and follow human intent:

"1. Training AI systems using human feedback

2. Training AI systems to assist human evaluation

3. Training AI systems to do alignment research

This involves both explicit intent given by an instruction as well as implicit intent like truthfulness, fairness and safety."

The first step in fixing a problem is to know you have the problem. The AI industry understands the situation, which scares the AI Pessimists.

As The AI Optimist, these are the seeds of hope. In the next pod, we'll cover possible solutions, and while it's early days, there are still reasons to be optimistic.

Takeaways on Bia and the Weakness of AI

* Training Data: AI models are as good as the training data. If the data contains inaccurate or unfair stereotypes or falsehoods, the model will reproduce them.

* Complexity and Objective Functions: The goals given to an AI often do not capture the full complexity of what it means for an outcome to be "good" or "true."

* Implicit Assumptions: AI models encode the biases or assumptions of developers or the societies where they practice.

* Over-Optimization: If an AI is trained to maximize user engagement, it might prioritize extreme viewpoints that aren't accurate, distorting the sense of "truth."

* Filter Bubbles: Recommendation algorithms create echo chambers where users are only exposed to similar viewpoints, polarizing opinions and creating a subjective sense of what is "true."

* Ethical considerations: How to create and adopt ethical frameworks dominates the current discussion. Dealing with bias, safeguarding fairness, and stopping harmful or misleading information is on top of everyone's AI lists.

AI models are tools created and deployed by humans, reflecting the complexities and challenges in human decision-making.

Creating AI systems that are fair, unbiased, and respectful of diverse viewpoints is an ongoing challenge.

Ethical Challenges

* Who Decides the Filters: The act of deciding what is a bias and needs filtering is a bias.

* Bias-Filtering Solution: Adopt a multi-perspective approach involving ethicists, various domain experts, and people from impacted communities must add to the design of these filters.

* Erasure of Minority Voices: Over-correction could erase cultural nuances or minority opinions.

* Bias-Filtering Solution: Design filters that factor in and preserve minority voices, including "exception rules" or "sensitivity thresholds."

* Transparency, Explainability, and Interpretability: Lack of transparency in how filters work leads to issues. Understanding how AI makes decisions and making it easier to understand is vital. Even if AI tells us how it decides, communicating in math, we need to be able to interpret this.

* Bias-Filtering Solution: Make the methodology and algorithms behind the filters transparent, open-source, and subject to third-party audits.

Belief in AI and the Placebo Effect: Let's Start Becoming the Solution Inside and Outside of AI

The good news is we are working on this and more problems, but it will be challenging because we can't untangle AI from the inevitable bias of society.

AI will not let us escape entrenchment in beliefs, as a mirror shows us an image we don't want to see of a visual and written world dominated by the few and not reflecting the many.

It's a good thing it freaks us out because that freak out is a change we can enact with AI. Because unlike what you read, it's not some digital savior.

Bias rules algorithms because they were never regulated in the early days. 

We all trusted them to be okay, yet here we are in an ad-driven market where algorithms extend impressions and ad sales, bots dominate user counts, and, even with attempts, keep getting in the way.

Living in echo chambers, divided societies worldwide and a younger generation in the US subjected to comparison, bullying, and polarization as a method for gaining and increasing influence.

Since we are starting today, it's easy to see why the AI pessimists rule. What's happened is seemingly unstoppable, not just from tech companies using data in nefarious and beneficial ways.

Still, governments use data as surveillance tools, with the Chinese being one of the only open and honest governments about how it tracks to maintain government control.

We stand at a turning point in AI.

Diversity is a problem. Understanding how AI works, transparency, fairness, and guiding AI to benefit us is a problem. Not bad data, but our data.

The cultures we all live in are steeped in cultural, political, and economic bias. Removing bias to protect people is a noble thought and likely doomed because you can't remove layers of bias when your data comes from content and perspectives that reflect society.

While many are disappointed by AI, seeing how it has let us down, it is simply an observer trying through rudimentary early ways of labeling, categorizing, and guiding AI to help us.

Before we all jump off a bridge of doom, many are trying to do better. And to understand what they are doing means understanding why bias is so important, so threatening, what has happened, and what will happen.

We are all raising a child called AI and learning how to give it directions and let it do what it does and move beyond old models of statistics and probabilities.

If you have a tough job, try doing the math and testing AI. Do we let AI learn data and do its own thing? That didn't work.

Do we try to interject our control and belief systems into AI? It doesn't always recognize the patterns we do, and how can it?

We all face these questions because AI permeates our healthcare, government, social media, and everywhere.

AI Bias isn't going away, and developers are working on new ways to minimize the bias and eliminate the harm. It's easy to avoid this, but we all must be involved and have a voice.

In the next AI Optimist Pod, we'll explore what's being done and the hope of removing AI Bias so it doesn't harm or negatively impact people.

It is a daunting task and a promising one with so many benefits. Yet many problems lie in our stereotypes, biases, and prejudices.

The more we create new words, images, and ways of being, the more AI will adapt.

We're interdependent with AI. Let's help it become better and, at the same time, recognize that many of the issues arise from the society we live in today.

Together, we can make a difference.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

It’s easy to talk about the AI game while clinging to the comfort of an echo chamber, repeating their old, comfortable business stereotypes. Bias is a mirage, especially against AI!

Reimagining AI’s Role: Cultivating Customer Bias in an Ethical Way

Using Customer Bias to Your Advantage: A 7-Step Guide 

Making Customer Bias Ethical for Them and You

Seven Simple AI Truths: The Customer Voice is All that Matters.

Of course, bias may undermine AI; the next pod will be about that. AI data includes preferences of all kinds based on the content we humans create. And we all have different stereotypes and ways of looking at the world that AI is showing us.

This bias is international, so AI in Asia is different from AI in the Arab World or the EU, US, CA, India, and China, impacting everyone and every country on this planet.

This pod is answering one of our debate questions about bias, examining how bias can benefit business, which means the power of customer bias.

Question 2: Bias in The AI Optimist Debate

How do you propose to address this issue to prevent the perpetuation of harmful biases and discrimination in AI-driven education and business applications?

ANSWER: Eliminating Bias implies you know the truth. Truth is often biased, especially in business.

Instead of fighting bias, we filter it, building a neural network focusing on a spectrum of bias.

Instead of force-feeding people biases, this filtering system will create an understanding of the multiple viewpoints. While people tend to choose their preferences, other diverse points of view challenge their thinking.

Finally, when/if AGI happens, this one will develop a multi-perspective understanding driven by bias and sentiment and taught in schools worldwide as a fact. Truth is seen differently by different people, and understanding these differences will help us all.

Reimagining AI’s Role: Cultivating Customer Bias in an Ethical Way

We’re diving into a new business game where the field is ever-changing, the players are algorithms, and the stakes are ethical and financial. 

We’re talking about the arena of artificial intelligence in business.

Remember the coach who took a flailing team and turned it into a powerhouse by playing to each player’s strengths? That’s the magic we’re gunning for.

In this arena, Artificial Intelligence (AI) is not just a new hire but a transformative force capable of reshaping how we approach marketing, sales, and customer engagement.

The dilemma we often face is akin to a strategic pivot in business: how to harness customer biases to benefit the company without crossing ethical lines.

Customer biases can be leveraged as assets if managed ethically and intelligently.

Imagine you’re developing a strategy that doesn’t just look at spreadsheets and metrics but considers the nuances of consumer behavior and preferences.

The challenge is ensuring that AI is an empowering organizer, harmonizing business ambitions with ethical considerations. It’s not about wielding AI like a sledgehammer but deploying it as a scalpel—precise, targeted, and ultimately effective.

Get this right, and you have a sustainable and ethical model that benefits customers and businesses. 

We’re piloting through a landscape where ethics and profitability aren’t at odds; they are two sides of the same coin.

Harnessing Customer Bias the New Way

In the desert, thirsty souls perceive mirages as accurate. In AI, businesses often need help, mistaking their bias for the truth of old practices being evergreen.

Imagine a business crushed by the weight of its old illusions, all in the name of staying relevant.

The AI mirage trickles down, infecting the algorithms that govern AI. Because what we ask for and what AI has scraped off the Internet is teeming with lousy business bias.

Bias is a chameleon, shifting colors depending on who’s looking and what they want to find. The most harmful biases for businesses are ones they create, separate from the customer.

Your Bias, My Bias: Switching Business Perspectives from Persuasion to Customers

Companies are detached from their customers, forming strategies based on outdated playbooks. The same ones must understand that they act on the stage driven by actual customer experience.

Imagine AI not as a parrot mimicking you but as a tool amplifying the hidden desires of your customers.

* Emails, landing pages, internal memos—all narrating the authentic story of what your customers want, not just what you think they want.

Avoiding your own bias in AI is a delicate art. ChatGPT isn’t a wizard; it’s a mirror reflecting society’s norms and prejudices as evaluated by data scientists.

Relying on it as a be-all-end-all solution is like expecting a mirage to quench your thirst.

The more you ask AI for the mirage you want, the more it reflects your version of the “truth .”

While we debate what constitutes “harmful” bias, remember that AI is as fallible as the data it’s trained on.

Whether it’s a ChatGPT assisting in writing copy or an AI tool automating your sales funnel, the biases you ignore today will lead you away from your goal and leave you in the desert.

Balancing Points of View and Listening to Customers

Biases aren’t just local folklore—they’re universal anthems. The bias inherent in an AI system in Asia sings a different tune than in the US or the EU.

The solution? A neural network fine-tuned to understand the music of different biases, acting as a mediator between clashing perspectives.

Thinking you can eliminate bias is naive; the aim should be to assume bias, not escape it.

After all, in the theatre of life, the scripts are never fixed; they evolve as the actors grow. So, should our understanding of business.

We aren’t merely selling products or ideas; we’re nurturing critical thinking and adaptability built on the customer experience, not our separate opinions.

Using Customer Bias to Your Advantage: A 7-Step Guide 

The Fault Lies Not in our Customers but in Our Bias

We’ve explored how most unknowingly make AI all biased and goofy, right? Now, let’s harness the power of customer bias to make your brand voice sing.

Your customer’s bias is what matters, not yours. Here’s how to get into your customers’ heads in a non-creepy way.

Step 1: Go Where the People Are

What Not to Do: Assume You Know Best

* Think you know what your customers want? Easy there, bias lover.

* The Right Move: Jump into social media, forums, and online communities where your customers hang out. Listen and learn. 

* Look at the books they read; review the 5-star reviews to know what they love, and the 1-star reviews to find out the critical part people missed.

Step 2: Follow the Feedback

What Not to Do: Neglect Opinions

* Ignoring customer feedback is like neglecting the gas gauge in your car. Not smart.

* The Right Move: Run polls, watch social discussions and videos, or even a quick DM to ask folks what they think about your brand or product.

*  Listen and don’t interrupt; the truth may hurt, and it’s also the key to finding their voice for your brand.

Step 3: Collage the Voices

What Not to Do: Go With the Status Quo

* Your old brand voice is comfy and often as outdated as a flip phone.

* The Right Move: Collect all the tidbits, phrases, and slang you’ve heard from customers to craft your brand’s voice.

* Stop inventing pain points and witness how customers explain your brand.

* It’s less about their pain and more about their lack of interest and attention.

* Stop solving problems you make up and follow the customers and customers to be.

* Focus on public information and permission; it’s easy to do this in stealth and violate the trust and ethical principles behind good AI. Don’t take shortcuts; make sure what you do honors the customers’ privacy and builds goodwill.

Step 4: Prototype That Voice

What Not to Do: Go All In Without Testing

* Jumping feet-first might work for swimming, but not here.

* The Right Move: Create a prototype voice for your brand and test it in a small campaign or with a social group.

* Start with 3-10 people; watch and listen to emerging patterns.

* Scale that up and adapt on the way.

* Finding their voice takes a little extra time; it’s good that you don’t want fries with some drive-by fake test. You are planting seeds.

Step 5: Get the Customer Party Started

What Not to Do: Assume You Nailed It

* You’re good, but you’re not that good. No one is. And it doesn’t matter (maybe to your ego but not to the customer).

* The Right Move: Use that new voice in a broader setting and actively ask for positive and negative feedback.

Step 6: Create a Customer Voice with AI with the best learnings.

What Not to Do: Be Stubborn

* “My way or the highway” doesn’t work in relationships or business.

* The Right Move: Use the feedback to refine your voice. Remember, repetition is vital.

* Systematize with AI to turn this into your Customer Voice. Run your copy through these learnings, and once you find the right path, create a separate Customer Voice to keep challenging and training your original findings.

* Business today is a dynamic game, not a short-term dance. Play the long game.

Step 7: Roll Out the Customer Voice and Make it Your Brand Voice

What Not to Do: Half-Heart It

* A partial rollout is like partially cooking a meal. Ew.

* The Right Move: Launch that new voice across all platforms once you’ve listened and tested enough.

* Ensure your business, employees, and outsourced team run their ideas, copy, and memos through the Customer Voice so it becomes the guide.

* Keep improving this voice and challenge it; encourage AI to praise your words, then provide a harsh troll-like critique from the customer.

* See both sides, love, and hate, and ride the middle to success.

Your Action Plan:

* Social Media Recon: Dedicate weekly to listening and gathering phrases and opinions.

* Quick Feedback: Use tools like Google Forms to create short surveys for direct feedback. Use social media to gauge interest and sentiment analysis (there are AI tools to do this!)

* Voice Crafting: Spend a day creating your new brand voice based on your learning.

* Social Group: Organize a small group or small campaign to test the new voice.

* Broad Testing: Apply the voice to a more extensive campaign and ask for critiques.

* Refine: Give another day or two to tweak based on the feedback.

* Full Rollout: Update all your social media, website, and outreach to the new voice.

Your customer’s bias is the one that counts. It turns browsers into buyers and maybe even into die-hard brand advocates. Get out of your head and into their words and experience.

Wake Up and Smell the AI Bias – Because you help create it!

Making Customer Bias Ethical for Them and You

You might think, “Hey, it’s a machine. Machines don’t have prejudices.” Wrong. Machines learn from humans, and we’re far from perfect.

* If you train ChatGPT on just a bunch of tech bro blogs, don’t be surprised when it starts sounding like one.

* Be aware that some have tested out and biased AI against race, gender, and physical location. Think things through before taking action to ensure you do the right thing.

* Quick Fix: Use data from all walks of life. Think of it like a potluck dinner; everyone brings something to the table.

1. Check AI’s Homework:

You ask AI to figure out what customers would love. Just like you’d check your homework, make sure the AI is doing its job well. This way, people get what they want!

2. Let AI Learn from Everyone

You wouldn’t color a picture with just one crayon? Same thing with AI. 

Make sure it learns from all kinds of people to give everyone what they like. Nobody feels left out, and sales grow!

3. Teach AI to Play Fair

AI can pick favorites without meaning to. Teach it to be fair so that everyone gets a chance to enjoy what you’re offering. Fair play keeps customers happy and coming back!

4. Ask for a Second Opinion

If you’re stuck on a complex game level, you’d ask a friend for help. If you’re unsure your AI is being fair, ask people for their thoughts. They might see something you didn’t, which will improve your business.

5. Keep an Eye on Your AI

Let’s say you are building a house. You’d check the foundation plans with an architect to make sure it doesn’t fall over. 

Same with AI—keep an eye on it to ensure it stays on track. People’s ideas of what’s good can change, so your AI should, too.

Make sure your AI is the best it can be for your business while ensuring everyone gets the proper treatment.

Quick Nuggets of Wisdom:

* Bias Happens: Just ’cause it’s a machine doesn’t mean it’s off the hook for bias.

* Variety is Spice: Different voices and perspectives make your AI better.

* Be the Good Cop: Ethical checks and balances aren’t just for the government; your AI needs it, too.

* Stay on Your Toes: Watch your AI like a toddler. You never know when it might scribble on the walls.

* Honesty Rules: Keep it 100 with your customers. Trust is the new currency.

ChatGPT or any other AI isn’t developing biases independently but learning from us. We’re the teachers here.

It’s on us to set a good example and make the tweaks needed to ensure AI efforts are as unbiased as they are helpful.

Many businesses increasingly rely on AI tools for customer outreach, market analysis, and internal communications tasks.

While AI can offer benefits, it can also inherit and continue biases in the data.

Action Plan:

* Audit Existing AI Systems: Use third-party audits to identify current biases.

* Collect Diverse Data: Start sourcing more diverse data for your AI system.

* Focus on Ethics as a Practice, not a Philosophy: Draw on all types of people, especially those who don’t always agree with you. Listen to them and evolve.

* Keep on Watch: Use AI to help monitor itself, flagging potential areas of improvement.

* Educate Customers and Team members: Roll out an education program to help your customers and those you work with to understand why addressing bias is vital.

Bias in AI doesn’t originate from the machine but is a reflection of existing biases. And if there is one bias you should follow, watch, adapt to, and grow with, it’s your customer.

It’s not that they are always right; it’s understanding how they look at the world.

Let AI help you understand and operate in the universe of the customer.

Seven Simple AI Truths: The Customer Voice is All that Matters.

* Ears Open: Listen more than you talk. Your customers are already telling you what they want.

* Ask Don’t Assume: Directly seek opinions; they’re the business equivalent of truth.

* Be a Chameleon: Adapt your voice to reflect your customer base – you may find several customer voices, not just one, depending upon the diversity of your audience.

* Test the Waters: Small tests save you from big headaches later.

* Critiques are Gold: Both praise and criticism are opportunities to learn.

* Keep doing it: Forget being perfect. Use AI to keep pushing out greater understanding.

* Why settle for what you think they want? When you’re sure, roll it out.

Instead of pretending to create a brand voice, your brand voice can grow from your customer’s voice with AI IF you learn to avoid your own bias. And listen to the customer’s bias.

That data we enter gives weak responses when it learns the same old things about marketing and predicts these as truths to your answer. Or is so lost that it hallucinates answers that aren’t real.

Truth is like the sun, and bias is like sunglasses. The sunglasses can make the sun look brighter or dimmer, but they can’t change the fact that the sun is still there.

And the customer is the sun, the center of your business universe. If you allow it to do its job, AI is here to help you understand them in ways you never could.

In this way, bias can be good if you take the proper steps and ensure you’re doing right by the customer.

In the next pod, we’ll explore the threat of bias in AI and how it impacts us all.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Your first prompt is like dipping your toe into the AI ocean, inspiring yet still wondering what lurks beneath the surface. Is it Terminator AI or the next coming of tech to free us from the drudgery of thinking and creating content?

Instead of wondering how the AI black box works, let’s start with your intent, the context of your question, and how to frame it.

Then follow up by challenging the response you get and constantly checking facts.

Note: In this podcast and article, I use the term “GenAI” for ChatGPT, Claude, Bard, and any other text-based LLM. We do not cover image prompts in this article, maybe in the future.

The Science of Asking Essential Questions = Good Predictions

Applying Your Magic to GenAI - chatGPT, Claude, Bard, et al.

Key Takeaways

TLDR Recommendations

* Begin with a clear intent for what you want to know.

* If you’re unhappy with an answer or find it too general, rephrase or specify further.

* Request multiple perspectives for a rounded view of controversial or subjective topics.

* Always fact-check information. GenAI is not close to perfect.

* Remember, the model is as good as the question. A straightforward question is more likely to give a complete answer.

Find the Magic in Your Questions, Not Just the AI Sea of Data

Imagine you’re chatting with a wise sage who holds the answers to almost all questions. But this sage speaks a unique dialect. It’s not impenetrable but one that needs the right questions.

Prompts result in predictions because that’s what GenAI does - provides projections based on the directions of the questions you ask.

It may have a data trove of information, but without specific guidance, direction, and context, it will make things up because you didn’t ask a good question. And didn’t follow up.

Regarding ChatGPT, or any AI, “Garbage in, garbage out” is the truth. If you are frustrated with bland answers, it’s not the AI. It’s the question you’re asking.

SOURCE: The Surprising Power of Questions - Harvard Business Review

The Science of Asking Essential Questions = Good Predictions

Questions are the compass, guiding the AI through its neural networks and fetching the answers we seek. If your compass is imprecise, you might go south when the answer you seek points to the north.

Questions shape reality. They define what we seek and what we find.

A vague question like, “Write me a good outreach email,” would lead ChatGPT into guesswork, and it might return with something equally ambiguous and mediocre.

Taking the next step and writing,

“Create a short marketing email to contact a possible partner. Write in a conversational, personable way. Use natural language and phrases that a person would use in everyday conversation about COMPANY that offers PRODUCT and get them to reply.”

That prompt, with a little context and specificity, would get you closer:

* Be specific.

* Please keep it simple.

* Improve the first response (see the previous Pod about Engage, Refine, and Iterate).

The Power of Questions

Before you start your prompt, explore the importance of questions.

* Asking good questions is core to communication, learning, and science.

* The type of questions you ask determines your response – let’s explore the best styles.

* How you frame a question leads to the kind of response you get and the quality.

* The psychology of culture, social, and individual biases impact the answers to questions.

Questions guide GenAI into crafting its response. Encourage curiosity by moving beyond the superficial surface of the question.

Do you need to improve your questions? Start with a simple prompt like:

 I’m researching XXX. Suggest three methods you need to create the best response and write three prompts I can use. Each prompt should provide a different perspective on the research, including the context.

Understanding the types of questions to ask can help you do these prompts yourself, though you can make a question and follow-up response with GenAI to create your prompts.

Example: 3 Good Types of Questions About AI

* Open-ended: Questions that cannot be answered with a simple ‘yes’ or ‘no’.

* Probing: Diving deeper into a topic, often a follow-up to an open-ended question.

* Leading: Looking for a specific response. These imply an answer and bias the response.

Remember, when asking questions, consider:

· The intended outcome – Are you looking to validate a point of view or find original answers?

· Who is the audience for this question, and what role is the question being asked from?

For example, asking about AI will vary significantly between beginners trying to understand the subject and a trained data science engineer.

The context matters and is most often overlooked, like:

> Temporal: when something happens, like historical or chronological circumstances.

> Spatial: The physical location where something occurs.

> Social: The cultural and societal links influencing how something is understood.

> Situational: The conditions relevant to the event or concept.

> Cultural: The beliefs, values, and practices describing a society or group of people.

> Psychological: The mental and emotional state of the individuals involved in a situation may affect how they perceive or interpret it.

Open-Ended Question Examples

Simple: “What do you know about the origins of artificial intelligence?”

Follow-up Open-Ended Question (aka probing): “Can you elaborate on the key figures or moments that you think were pivotal in its development?”

Open-ended questions allow for various responses and encourage detailed information or opinions.

Probing Question Examples

Simple: “Was the Turing test significant in the history of artificial intelligence?”

Follow-up Probing Question: “What other milestones or breakthroughs were equally or more important in shaping the field of AI?”

Probing questions dig deeper to explore the nuances of the topic, encouraging specific information or clarifying the initial response.

Leading Question Examples

Simple: “Was Alan Turing a foundational figure in the development of artificial intelligence?”

Follow-up Leading Question: “Is is true that without Turing’s work, the field of artificial intelligence wouldn’t be where it is today?”

Leading questions guide the response toward a specific answer or viewpoint. These are biased, of course, and using a few of these to get multiple perspectives helps.

For example, after asking the above follow-up, ask:

“Now disagree that without Turing’s work, the field of artificial intelligence wouldn’t be where it is today?”

Suggesting multiple perspectives is wise in any research, even marketing, where you get a critical and positive response.

These questions all aim at different kinds of information related to the origins of artificial intelligence.

Crafting Your Prompts - Designing Questions and Follow-Ups

Bias gets in the way of asking a good question, which is why leading questions can impact your answers from GenAI. 

Asking it for an opinion is inviting it to look at other’s views, and this is where hallucinating happens, which can undermine what you’re doing:

* Challenge assumptions – your own when asking questions and GenAI’s answers.

* Get a broad perspective by asking for different opinions in follow-up questions.

* Know your own biases; personal beliefs influence responses.

* Avoid confirmation bias (asking answers to confirm what you believe).

* Make the question clear and concise.

* Test questions out and adapt them if the response you get is poor.

Be sure to consider how you ask the question:

* Do your words trigger an emotional response?

* Did you ask ‘why’ and ‘how’ questions? (“Why did this happen?” asks for an opinion that GenAI doesn’t have, vs. “How did this happen?” which focuses on facts.)

Action Items for Writing Prompts

* Create clear and specific prompts.

* Experiment to get varied responses; often, asking the same question twice will get you different answers.

* Understand the limits of GenAI’s knowledge and understanding.

* Remember the importance of open-ended questions for thorough answers.

Asking the right questions is a skill you can learn with GenAI. This key is why AI is a co-pilot to help you create better answers than without it.

Your part is learning to ask good questions and keep those prompts in a file as you grow, learn, and share with others.   

Get creative with TextFX by Google - Play with this to use Similes and Scenes!

Applying Your Magic to GenAI - chatGPT, Claude, Bard, et al.

The GenAI seemingly magic response comes quickly and confidently, filled with references. But wait, that’s not right. Is this thing hallucinating, or maybe it’s me?

An AI prompt and a magic trick can create an illusion of control and mastery.

> An AI prompt by providing a framework for generating text or images that seem to be the product of human creativity. It’s not. What you get is a probable response based on what others have created.

As I shared in the previous Pod, this can sometimes create average answers.

> A magic trick using sleight of hand or misdirection to create the illusion of supernatural powers does the same thing when it’s not magic. It’s simply a process hidden from plain sight.

The key to generating stellar content is asking the right question, where you need to be a magician. Instead of vague, subjective questions looking for an answer that you expect, be different.

Unlocking the power of GenAI comes in asking the right questions and following up to take a broad view down to specific steps with multiple points of view.

The magic comes when you ask the right questions and don’t ask for opinions.

The roadmap to asking good questions lies in strategy:

> Begin with a broad, simple question, narrow it down, like zooming in on a digital map.

> Chisel away based on the response, delving deeper, seeking clarity, and

> Ask for real-world examples when things seem abstract.

> When treading into questions needing opinions, request different perspectives.

GenAI’s power lies not in its ability to think for itself but in offering views based on training data. It’s like having the voices of a million authors, scientists, and thinkers ready to present their take on your question.

Please don’t ask what it thinks because it doesn’t think. It predicts what you want!

Best Practices

* Be specific. Vague questions get general answers.

* Break down complex questions into simpler ones. Use follow-up questions to get into the challenging issues. I’ll often use 4-5 more questions.

* Ask for details, and work on the tone of the response to make it more formal or less formal.

JASPER Example: ADVERTISING prompt

“[Use sentence fragments and figurative language. Write as a master of brevity would. Frequently use short pithy sentences that pack a punch]”

* Watch your language to avoid biased responses. Opinions are a weakness of GenAI unless the question requires opinions, then ask for the various sides of the opinions. 

* Do not write an opinion piece because you’ll get someone else’s opinion, and it’s usually a weak article.

Best Examples of Questions

* Open & Clear: “Explain the theory of relativity in simple terms a 10-year-old would understand.”

* Be Specific & Contextual: “What were the primary causes of World War I from a European geopolitical perspective?”

* Sequential: Start with a general question and dive deeper based on the response. For example:

* “What is quantum mechanics?”

* “How does the double-slit experiment demonstrate quantum phenomena?”

* “Can you explain the concept of superposition in that context?”

What to Ask and What Not to Ask

* Do ask Specific factual questions, requests for summaries, language translations, etc.

* Don’t ask Personal advice, subjective questions, or illegal/contentious topics.

Clarity and Specificity

* Frame your question in a clear context. E.g., Instead of “What’s AI?” say, “What does AI stand for, including specific examples of the different AI available?”

* Avoid using slang or informal phrases unless you’re inquiring about them.

Multiple Perspectives

* Always ask the model to provide multiple perspectives. Use GenAI to both support and attack your viewpoint. Do this for marketing especially. 

* Encourage it to be supportive and critical so you get a rich response, not a pat on the back.

DEBATE PROMPT EXAMPLE: The AI Optimist

I want to practice my knowledge of AI frameworks as The AI Optimist in a 10-question debate with you, the AI Pessimist. You’ll play the role of a pessimistic AI person with a scientific viewpoint and not critical of others who will prove my ideas falsifiable and come up with an alternative AI in the future hypothesis.

I’ll play The AI Optimist, an instructional designer and growth developer for 30 years who is looking at how AI might benefit humanity in education and business.

The goal is to practice understanding the AI Pessimist’s point of view and to create my hypotheses based on their input and opposition to my scenario. Create a scenario in which I can practice my response and intelligent discourse with the opposition.

I should be labeled as a futurist, a dreamer, a hollow view, as not understanding how dangerous AI is and not taking risks and threats seriously.

You start the debate give me dilemmas or problems during this 10-question debate to challenge my thinking.

After ten questions and interactions, set up a consequential choice for me to make. Then wrap up by telling me how I performed as the AI Optimist including good points and what I can do better next time.

Do not play my role. Only play the AI Pessimist. Wait for me to respond.

Lessons Learned from Prompts

* Ambiguity: Vague prompts lead to general or ambiguous answers. Specifying what you need is essential.

* Assumptions: Sometimes, you can assume that GenAI understands a previous question. Make sure you ask related questions together in one session. Open a new session to explore a new topic. Always provide relevant context.

* Limitations: ChatGPT stopped gathering data in 2021. Numerous lawsuits challenge GenAI’s ability to collect data on specific subjects. Scientific topics, in particular, are weak in getting trusted answers. Given time, this will likely change, but for the short term, don’t expect it to know everything.

* Check sources: GenAI is notorious for sharing inaccurate or nonexistent sources. It’s not lying; it just doesn’t have the data, and remember the law firm who didn’t do this and lost their jobs. Check your sources!

Mistakes

* Leading the Model: Ask leading questions only if you have the skills to do it and are prepared to provide more input. For example, “Isn’t it true that...” might not always give you the most objective answer. You get what you ask for.

* Assuming Infinite Knowledge: The model doesn’t “know” everything. It’s based on the data it was trained on.

* Being Too Brief: While brevity is often good, too simple or too much jargon can make it less accurate.

Godmode - Try your prompt on Bard, ChatGPT, Poe, Anthropic and more

Key Takeaways

By now, you might think, “Okay, so I get it—ask the right questions. But how exactly?”

Here’s the actionable bit, the ‘do-it-now’ steps to ensure your next interaction with GenAI is a good one:

* Draft Your Questions: Consider what you want to ask before posing it to GenAI.

* Start Broad: Lay the foundation. If you’re curious about black holes, start with “What are black holes?”

* Zoom In: Based on the first answer, dive deeper. “How do black holes form?” followed by “What’s the significance of the event horizon?”

* Seek Multiple Views: Ask GenAI to provide varied views.

* Feedback Loop: Not satisfied? Reframe, rephrase, and ask again.

* Ask for Examples: After a theoretical or general answer, ask for real-world examples or case studies.

GenAI is a mirror, reflecting the quality of your questions. It’s not just about asking; it’s about mastering the art of cross-questioning.

Notice the main difference between good questions and bad ones.

The good ones are led by. “Who,” “what,” “when,” “where,” “why,” “how,” and “which” are not led by a verb.

Remember, you can ask GenAI to create prompts; there are tools to develop good prompts, and so many sources of prompts are available for free.

You don’t have to create the first prompt yourself, but this is not too difficult as you learn to do it.

Like a baby taking its first steps, unsteady and awkward, with practice, it becomes second nature if you know how to ask the right questions.

Thank you for reading The AI Optimist. This post is public so feel free to share it.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

How does an old tool like email even last in the time of AI? Because everybody has an email, and as all the Creator Economy have recently discovered, it’s a powerful way to keep in touch that you control.

So you build a following on social media. You see others with thriving businesses driven by email, so you start. —staring blankly at an email draft, fingers hovering over the keyboard, unsure of how to begin, what to say?

Go to ChatGPT, right, but what questions do you ask beyond writing me an email? What is so important to understand about AI? It would help if you asked the right questions.

A common misconception is that the AI’s marketing language, taken from the vast trove of emails others have written (often poorly), is the gold standard. 

And it reflects what’s most common, not what’s most effective. If you want your emails to stand out and resonate, you’ve got to infuse them with more than just ChatGPT’s initial suggestions.

One of the challenges in creating any content, especially marketing, is understanding what an excellent AI-generated email should include. Many make this mistake, having negative ideas about marketing.

It’s like setting off on a road trip. Sure, you’ve got the car (ChatGPT) that can take you anywhere, but you—the driver—need to decide the destination and the best route to get there. 

By actively interacting and directing the conversation, you can guide ChatGPT away from the ‘main roads’ of average language and onto the ‘scenic routes’ of tailored impactful communication.

But why is asking the right questions so critical? The answer is simple. ChatGPT responds based on the cues you provide. 

Vague questions might lead to generic answers—like expecting a chef to create a gourmet dish without specific ingredients. So, to truly leverage this AI’s potential, you’ve got to dig a little deeper, refine your approach, and sometimes even challenge its first responses.

ChatGPT is a tool, a treasure trove of data and language patterns. But it doesn’t know the best way to communicate your unique message. That’s where you step in. Your guidance and strategic questioning mold its responses to perfectly fit your vision.

So, as we delve into this interactive email case study, remember you’re not just a bystander. You’re an active participant, a co-creator. Together with ChatGPT, you begin crafting emails that convey information and capture essence and intention.

Ready to start? The power is in your hands—let’s put it to good use!

Craft your email as a conversation, not a monologue. By blending AI’s breadth with your straightforward questions, create tailored messages that resonate deeply and stand out compared to generic emails that sound like everyone else.

Questions have the potential to guide thinking, stimulate curiosity, and promote more profound understanding.

The type of question you ask will affect the way ChatGPT responds.

* It is essential to be aware of your own biases when asking questions.

* Weak emails are standard because people don’t understand what a good one is, which is why this case study will help.

* Questions should be clear, concise, and free of jargon.

Case Study Overview: Writing an AI-generated Email to Contact a Possible Partner to Promote an SEO Tool

For partnerships, outreach involves contacting busy people who have little time. You need to get to the point in the subject line, and first two sentences, as that is all people will read before deciding to delete your message or respond.

What follows is how I used ChatGPT to create an email, the inadequate responses I had to improve with follow-up questions, and applying this same method to anything you make with AI.

Here’s my detailed initial prompt, which also created a mediocre ChatGPT, as you’ll see below.

The email involves a Subject Line and the actual words used in the email. The Subject Line is what you see before you open the email and is one of the deciding parts of whether anyone even reads the email. Remember this importance as you proceed.

FIRST PROMPT

Acting as an affiliate manager for an SEO tool company creating search content with AI, write a 3 paragraph email in a friendly and professional voice, inviting a longtime SEO expert who rarely recommends tools into the affiliate program and offering a private trial to make sure the expert likes the SEO AI Tool. Be sure to include that both the affiliate program and the SEO expert are quite picky about who they choose to partner with, and encourage a follow up meeting or questions so both sides can see if it’s a fit. 

ChatGPT First Response

Subject: Exclusive Invitation to a Unique Partnership with Our AI-powered SEO Tool

Dear [SEO Expert’s Name],

I hope this message finds you well. I’m reaching out to you today because I deeply admire your expertise in the field of Search Engine Optimization. As an authority in this domain, your insight and discernment have been invaluable to the industry, and we appreciate your judicious approach to recommending tools.

I’m writing as the Affiliate Manager for [Your Company], a leading SEO tool company that’s revolutionized the field with advanced AI-powered technology. We’ve been quite discerning in our choice of collaborators, much like you’ve been with your endorsements. Given your profound understanding of SEO and our shared commitment to quality, I’m excited to extend an invitation to you to join our exclusive affiliate program. I’d also like to offer you a private trial of our SEO AI Tool, allowing you to experience firsthand the innovation and efficiency our tool can bring to any SEO work.

We understand the importance of ensuring a fit that aligns with both our values, and hence, would welcome an opportunity to discuss this partnership further. If you’re open to exploring this potential collaboration, or if you have any questions about our tool or the affiliate program, please don’t hesitate to reach out. I’d be more than happy to arrange a follow-up meeting at your convenience to address any inquiries you may have and to walk you through the unique features of our SEO AI Tool.

Looking forward to hearing from you soon, and I truly believe this partnership can bring about incredible value to both our audiences.

Best regards,

Readability Stats mean how easy is this to read? It reads at a 12th grade level when 5-7th grade is preferred. Notice the words per sentence, too much!

And the Flesch Reading Ease is 44.8, too low!

This email is long and starts with the words many uses, which gets you put into spam: 

“I hope this message finds you well.”

I hope this message finds you well….snore.

Yes, it’s good to care, but this is not nice when reaching out and seeing this or a similar line (see the bottom of this article to see what not to do).

It’s trying to prove you’re a nice person. Google will often immediately put emails with this or similar opening sentences into spam because spammers use it.

Like creating anything with ChatGPT or any AI, you must ask follow-up questions to get the best content. Please read this and know that I know how to connect with people using email for over 20 years, so read my instructions to understand what a good email should contain. 

Follow-Up Question

Rewrite this to be half the length, using bullets to outline key elements and avoid polysyllabic words and sales sounding language.

This needs to be more genuine, personal, and approachable, and less of the verbose language and praise for the SEO expert....

Inject curiosity language to encourage exploring and gaining feedback from the SEO expert, even if they don’t partner.

2nd ChatGPT Response

Subject: Invitation: Try Our AI SEO Tool & Possible Affiliate Partnership

Hello [SEO Expert’s Name],

I hope you’re doing well. I’m [Your Name], Affiliate Manager at [Your Company]. I’ve seen your work, and it’s impressive. We’d like to talk about a potential partnership.

Here are the key points:

● We’ve developed a new AI SEO tool.

● We’re being selective about who we partner with, just like you are with your recommendations.   

If you like it, we’d be thrilled to have you join our affiliate program.

Let’s chat about it and answer any questions you might have. I’m available for a call or video meet whenever you’re free.

Look forward to hearing from you, 

Readability Stats mean how easy is this to read? It reads at a 7th grade level, better!

And the Flesch Reading Ease is 67, much better!

I made a mistake by not instructing it not to include that awful first sentence.

Follow-Up Question 3

Do not use “hope you’re doing well” or anything like it for the first sentence, avoid saying who the sender is and company name, treat this more like a text message than an email.

Add a sentence on how the SEO AI tool isn’t full of hype like most AI and has been around for 7 years, and we’d like to earn the SEO expert’s trust by showing them the tool, giving them a monthly recurring commission of 25% on any sales, with a 60- day cookie and last click attribution.

Rewrite and inject curiosity language to encourage exploring and gaining feedback from the SEO expert, even if they don’t partner. We learn and the SEO expert learns in a quick review, with the goal to be affiliate partners.

Rewrite in a casual and conversational tone.

3rd ChatGPT Response

Subject: Invitation: Private Pass🔹Test Drive AI SEO Tool & Link Up?

Hey, as you review SEO tools, what about one that cuts tedious SEO content creation by 40% or more?

Our AI Tool has been delivering SEO results for 7 years, minus the AI hype. The net result is content that Google eats up like (digital) candy.

👉 We’re picky about our affiliate partners, just like you’re selective about the SEO tools you endorse.

And your experience and savvy is why we’re reaching out:

🔹 Please take a free spin with this Private Pass; see if it earns your trust. - URL

🔹 Like it? Consider joining our affiliate program.

🔹 Perks include a 25% recurring commission on any sales, 60-day cookie click attribution.

Keen to talk more and answer any queries you might have....

This is the best it could get with ChatGPT; I did some rewriting, but it’s not bad. This email is short, focuses on the person being contacted.

This email could be improved by being more personal, and less talking about the SEO tool. Talk to the person getting the email, what’s important to them, and get them to reply or respond so THEN you can share what’s important about you.

Remember it’s always about who you are sending it to, and not about you. That’s a key to any email, marketing or otherwise - even someone you work with.

Takeaways

1. Good Questions, Good Responses: be specific, let ChatGPT know what role to play – in this case, the one sending the email - to gain focus.

2. Set the tone: AI needs to know what you want the response to sound like. Be clear. Details!

3. Ideas are where you save time – get new ideas and angles to create your content. 

My best advice is to take what you get and personalize it in your voice, who you are, that’s what sets you apart.

Conclusion & Recommendations for Writing Content with ChatGPT - Emails and all

• Begin with a clear intent for what you want to know.

• If you’re unhappy with an answer or find it too general, rephrase or specify further.

• For contentious or subjective topics, request multiple perspectives to get a rounded view.

• Always fact-check critical information. ChatGPT can provide a lot of information but isn’t infallible.

•Remember, the model is as good as the clarity of the question. A well-phrased question is more likely to yield a comprehensive answer.

Merging AI and Authenticity in Email Mastery

This case study in the art and science of email writing in collaboration with ChatGPT shows how to make it work for you. Let’s revisit the cornerstones of our discussion:

* Start with Authenticity: Crafting an email should feel like a dialogue. On this two-way street, genuine connection takes priority over broadcasting a one-sided message.

* AI as a Tool, Not a Crutch: ChatGPT offers rich information and linguistic patterns. However, guiding and refining its responses is essential to ensure they align with your unique brand voice.

* The Importance of Question Precision: Make sure your questions are well-defined. A vague question may lead to a generic answer, whereas specificity can be the key to tailored insights.

* Blend the Breadth and the Unique: While ChatGPT provides knowledge, your individual touch and perspective make emails resonate.

* Challenge the Initial Response: Don’t just settle for the first answer. Dig deeper, ask follow-up questions, and steer the AI toward the nuanced response you’re aiming for.

To harness the power of email, work as a copilot with ChatGPT. The real magic happens in the dance between human creativity and tech prowess. 

Next time you write an email draft, remember: Don’t just throw a question into the abyss and hope for brilliance. Engage, refine, and iterate. 

The key isn’t in asking a question—it’s in the art of the follow-up. With this partnership approach, you craft emails that inform and inspire.

BONUS – Email Best Practices to Understand for ChatGPT

* Your Subject Line: When someone gets your email, the first thing they do is ask, why is this important?

This is where the subject line comes in; most businesses focus all their time on the email’s copy and look/feel, not realizing that if the subject line stinks, no one reads it. 

* Make Your Email Like a Postcard: One of the common questions I get is how long an email should be.

While long emails can sometimes work, keeping it brief, like a postcard, is most often recommended. Be sure to put a blue link at the top and bottom of the email, and don’t rely on graphics.

Often these images are hidden from people up front, and the email should work with and without graphics.

* Your Subject Line; Do I repeat myself? Of course, I do, because you need to think about this first. So many send an email at the last minute and rush to write a subject line as if it’s the last chore.

It is the first task, the defining moment of whether your email is opened, the real test you are facing. Check out subject lines constantly and play with them to encourage people to open by adding value.

Tricking them is easy, but if you trick and disappoint them, they will hit the Spam button on you quickly.

* Your First Sentence: This is a lesson from journalism and direct marketing. Your first sentence is the second barrier and should flow from the subject line.

Just like someone searches for a keyword at Google and looks for that keyword on the page they go to, a person who sees a good Subject Line wants that headline immediately reinforced in the first sentence.

Lose them here, and you not only lose their interest, but boredom also leads to hitting the Spam button, which hurts your chances of getting delivered to others.

APPENDIX

Bad (and Most Common) Email Ledes (first sentences)

DO NOT use these, and see how often you see them!

Hello my name is FIRSTNAME from COMPANY TAGLINE)

Hope you are doing great!

I hope you don’t mind me being direct but I just had to ask

I hope all is well with you

Hope your week is off to a great start

Hope you had an amazing weekend

(HOPE: To wish for a particular event that one considers possible.)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

The Silent Observer: AI’s Evolution Over the Past Decade

From the moment you wake up, you’re being watched, not by another person but by an invisible presence: algorithms powered by artificial intelligence (AI). 

Over the past decade, the rise of AI has seen digital footprints become goldmines for large tech companies and governments.

For teenagers, especially, the allure of social media is undeniable. Beneath the surface of sharing, liking, and commenting are algorithms that record their interactions and influence perceptions and decisions.

There have been numerous cases of governments buying and selling data. This data, if taken directly, would violate numerous privacy laws. Yet, the first wave of AI has opened a backdoor for such transactions.

The AI behind our screens is adept at creating addictive experiences and curating content trapping people in ‘echo chambers.’ This dramatically limits exposure to diverse views, making the digital experience less enriching.

Reimagining Privacy with HarmonAI:

Ushering in a New Dawn

Picture a world where technology doesn’t peek over your shoulder but shields you from prying eyes. Where AI acts like a friendly companion, guiding and assisting us? Let’s capture that vision with an idea called HarmonAI!

Remember the First Wave?

The pioneering days of AI brought unparalleled convenience. Your phone knew the nearest pizza place, and your smart speaker played your favorite tune before you even asked. 

But at what cost? Every click, every pause, every search—tracked, stored, and analyzed. It seemed like a fair trade-off, right?

What if AI could still offer that convenience without compromise?

HarmonAI:

A Fresh Start with AI Privacy Serving People First

Imagine an AI so advanced it understands the value of discretion. Instead of whispering your secrets into the vast databases of big tech companies, it safeguards them like a loyal friend. 

This AI doesn’t sell you out to advertisers or let governments peek into your daily routine. Instead, it stands guard, ensuring your digital world is yours.

Imagine HarmonAI: Emphasizing harmony between humans and machines. Protecting your privacy.

Unleashing Your Potential

Have you ever hesitated to search for something, fearing it might lead to unwanted ads or expose you to surveillance? 

What if you never had to feel that way again? With AI putting people first, we can re-explore the vast oceans of the internet without hesitation. 

How many more innovations, connections, or aha moments await in such a liberated world?

Pop Quiz: What would you do if...

· Your AI could make recommendations without storing your personal data?

· You could confidently discuss sensitive topics, knowing your conversations aren’t being monitored or sold?

· Your digital actions led to personal growth without any external entity knowing without your permission?

Reimagine Privacy

Now, think of more than just individual benefits. Consider a society where privacy is not an afterthought but a fundamental layer. 

Schools integrate AI tools without risking student data. 

Healthcare utilizes AI-driven predictions while preserving the safety of personal health information. 

The possibilities are endless and positive.

The Future is Ours to Control

The initial wave of AI got privacy wrong. We have the tools, the expertise, and the motivation to reshape that narrative. 

Let’s redirect our focus toward a people-first approach. We’re not just regaining privacy but crafting a digital future that aligns with human values.

In this vibrant new era, the question isn’t, 

“What data are we willing to give up?” but rather, 

“How can technology amplify the best of humanity without any compromises?”

Journey together into this inviting future where AI respects our privacy and celebrates and safeguards it. 

Are you on board?

Charting New Horizons with AI: Progress Over Preferences

“Without deviation from the norm, progress is not possible.”

 — Frank Zappa

Finding uncharted territories, fresh ideas, or even confronting opposing viewpoints is challenging in a world dominated by algorithms that thrive on past behaviors and comfort zones. 

What if the future of AI is geared not to echo back preferences but to propel us toward unexplored potentials and paths?

Four User-First Approaches to Privacy:

* Consent-Led Data Sharing: Before any application or service accesses data, it must specify why it needs the data and how it will be used. We grant permissions based on this clarity, ensuring we’re always in control.

* Privacy Profiles: Just like we have different profiles for work, gaming, or browsing, we can set other ‘Privacy Profiles.’ For instance, a ‘Shopping’ profile might share preferences and sizes for better product recommendations. A ‘Browsing’ profile might restrict almost all personal data.

* Data Lifespan Settings: Allow us to set expiration dates on data. For instance, if you visit a website to buy a gift, set the data (like browsing history and product searches) to “expire” or auto-delete after 24 hours, ensuring temporary actions don’t influence long-term recommendations.

* Transparent AI Reporting: Whenever AI processes data to make a recommendation or decision, we can view a report of which data points influenced that AI decision. This allows us to better understand how data is being used and even correct AI if it’s working off of wrong assumptions.

The New Age of Data Reckoning is Here

With tailor-made systems and principles in place, we can usher in an era where AI and privacy coexist, delivering on the promise of tech advancements without compromising individual rights.

3 Immediate Ways to Protect Consumers:

* Legislative Checks: Governments should enact strict legislation ensuring transparency in how AI algorithms collect, process, and use data. Fines should be hefty for companies or entities found in violation.

* Educate the Masses: Education programs about AI’s impact should be widespread. Everyone must know how AI affects their daily lives, especially impressionable teens, from schools to community centers.

* Opt-out Mechanisms: Every platform using AI algorithms should provide users an easy option to opt out. This would mean that we choose to not have data used for anything beyond the primary service we signed up for.

The Tangled Web of AI: Solutions for a Safer Digital Future

To ensure that AI serves us rather than the other way around, here are possible solutions:

* Open-source AI Ethics: All AI algorithms, especially those touching the public sphere, like social media, should be open-sourced. This promotes transparency and allows independent experts to scrutinize and call out biases or harmful practices.

* Break the Echo Chambers: Platforms should consciously develop algorithms that promote diversity in content rather than mere confirmation of pre-existing beliefs. This could be done by mixing content from different sources or perspectives, ensuring a more rounded digital experience.

* AI Watchdogs: Appoint independent AI watchdogs who regularly audit the algorithms of tech giants, ensuring they aren’t being tweaked to induce addiction, misinformation, or any other harmful behavior.

* Empower the User: We can adjust the aggressiveness of the algorithms. If someone feels like they’re getting too much of the same content, they simply dial back the algorithm’s influence.

* Universal Data Rights: Create a universal declaration of digital rights, ensuring every individual’s right to their own data. This would mean governments and corporations need explicit consent to use anyone’s data, closing backdoors and gray areas in data collection.

The path of AI is undeniably intertwined with the future of humanity. 

As AI continues to evolve and influence every aspect of our lives, it’s essential to understand the challenges, navigate them wisely, and ensure that we, as a society, don’t become mere data points in some algorithm. 

We must always be the ones in control.

Opt-In AI: Ensuring Privacy as the Default, Not the Exception

1. Fundamental Right to Privacy: The inalienable right to privacy is at the heart of the argument for an opt-in AI. Historically, humans have always valued their personal spaces and private lives. In the digital age, this translates to data privacy. If data is the new oil, then our personal data is akin to our reserves. Access to it should not be granted by default.

2. Consent over Assumption: An opt-out system inherently assumes consent. It presumes everyone is okay with using their data unless they say otherwise. This is a flawed premise, especially when many may need help understanding the impact of these default settings or may miss the seemingly hidden opt-out option.

3. Restoring Trust in Technology: Data breaches, misuse of personal data, and other controversies erode public trust in tech giants. Moving to an opt-in system signals respect for our privacy and restores faith in the industry.

How to Automatically Protect Your Data: 

The Unrealised Power of Anonymization and Tokenization

* Local Data Anonymization and Tokenization:

How It Works:

* Upon any data creation or capture on a device, the data is immediately anonymized locally, ensuring original personal identifiers are stripped off.

* Post-anonymization, a unique token is generated for this data, which acts as a placeholder or reference without carrying any personal information.

* When the data is requested by any application or service, only the tokenized version is shared.

* Dynamic Action-Based Token Generation:

How It Works:

* For every action, a new, temporary token is generated. This token doesn’t contain identifiable information but represents the action or preference for that specific instance.

* Like a session ID, it expires after its purpose is served. For instance, if you search for “best running shoes,” a token representing “interest in sports footwear” might be generated and sent to ad networks or recommendation engines. Your identity and the exact search would remain unknown.

* Decentralized Privacy Networks (DPN):

How It Works:

* Similar to how VPNs route traffic through different servers, DPNs route data requests through multiple peer devices in a network.

* Before data is routed, it’s anonymized and tokenized. By the time it reaches its destination, it’s been through several ‘hops,’ making it extremely difficult to trace back to the origin.

* As there’s no centralized server, there’s less vulnerability to hacking or centralized data collection.

Redefining AI’s Role: A Personal HarmonAI

Imagine an AI that primarily resides on your device, works for you and is designed to enrich your life rather than serve ads.

1. Personalized Content without Privacy Invasion: With access to your local data, this AI can curate content - from news articles to music playlists - based on your interests. All this without sending your data to a remote server or selling it to the highest bidder.

2. Learning and Growth: Instead of showing content that keeps you scrolling mindlessly, an AI designed for personal enrichment could suggest educational materials, tutorials, or even trivia based on user interests. It could remind you of long-term goals or hobbies you’ve neglected, helping you become the best version of yourself.

3. Improved Productivity: The AI can analyze your work habits, providing insights into your most productive hours and tasks that take up most of your time or suggesting breaks when it detects decreased efficiency. This transforms AI into a tool for personal betterment rather than a distraction.

4. Offline Functionality: An AI operating primarily on a device doesn’t need constant internet connectivity. It can function offline, ensuring users get recommendations, reminders, and insights even in areas with poor connectivity.

5. Diverse Monetization Opportunities: Instead of relying on an ad-driven revenue model, companies can monetize such AI agents through premium features, direct sales, or even by offering specialized training packs (like a productivity booster, language learning assistant, etc.)

The digital landscape has, for too long, been dominated by an ad-centric approach, often at the expense of user privacy and well-being. 

Making AI an opt-in service ensures privacy is respected by default. By reimagining AI as a personal agent focused on enrichment, we can harness its potential in a manner that genuinely benefits us and society.

HarmonAI 2030: Your Personal Growth Catalyst

Imagine waking up to a virtual assistant, HarmonAI. Instead of scheduling your day or reminding you of tasks, she’s there to challenge, enlighten, and foster personal growth.

1. Morning News Recap: Alongside the usual headlines, HarmonAI curates a news story from a source you wouldn’t typically read. “Here’s an opinion piece from ‘Global Perspectives,’ a source you haven’t interacted with. Thought it might offer a fresh viewpoint,” she suggests.

2. Lunchtime Learning: Instead of your favorite podcast about tech innovations, HarmonAI proposes, “How about a dive into classical European history today? It might give you fresh perspectives for your tech presentation next week.”

3. Evening Reflections: You plan to watch a movie after dinner. “I’ve shortlisted a foreign film that’s critically acclaimed. It might be a different flavor from your usual choices, but it offers a new cultural perspective,” she offers.

In this scenario, HarmonAI acts on two primary principles:

* Broadening Horizons: Instead of capitalizing on your comfort zones, HarmonAI constantly challenges your choices. This doesn’t mean ignoring your preferences but weaving in variations to provide a broader perspective.

* Feedback and Growth: After each suggestion, HarmonAI. asks for feedback, not to recalibrate to your comfort zone but to understand your level of intellectual and emotional stretch. “Did the foreign film feel enlightening or just confusing?” Based on your feedback, HarmonAI might suggest an intermediate film, ensuring growth without overwhelming you next time.

Breaking the Echo Chambers

HarmonAI’s underlying code is designed differently. Unlike her predecessors, the primary function isn’t to maximize screen time or promote addictive behavior. 

Instead, her North Star metric is the “Personal Growth Index.” This means:

* Intellectual Diversity: Pushing us to explore diverse topics, fostering a more well-rounded worldview.

* Emotional Resilience: Recommending content that provokes thought, confronts biases, and challenges existing beliefs.

* Skill Advancement: Proactively suggesting courses, readings, or tutorials that help us venture into new territories.

For users seeking insights, HarmonAI provides a monthly growth report.

 “In August, you explored three new subjects, confronted five challenging viewpoints, and progressed in two skills. However, you’ve stayed within your comfort zone in arts and literature. Would you like some recommendations there?”

In a world where AI is increasingly shaping our experiences, aligning it with the growth and potential of human beings ensures a future of progress, enlightenment, and actual personal development. 

For humanity to truly progress, we must welcome deviations, embrace challenges, and cherish the unfamiliar. 

With AI as our ally in this journey, the horizons of growth are unlimited.

Navigating the Digital Renaissance: Embracing the Vision

The vision of AI as a collaborator rather than a subservient entity is not just an aspiration but a necessity in an interconnected digital world. 

HarmonAI emphasizes harmony, mutual growth, and shared progress, shedding light on how we need to perceive and integrate AI into our daily lives.

And this ideal stands in stark contrast to the current ecosystem. 

Big tech firms, driven by ad monetization, have developed algorithms to capture attention, often at the expense of user well-being. 

Government surveillance has raised privacy concerns, limiting the potential for AI systems to be transparent and open.

Yet, as history has shown, societal needs and pressures can lead to significant shifts:

1.    Consumer Demand and Advocacy: Companies will face increasing pressure to adapt as public awareness grows regarding the pitfalls of attention-driven models and privacy invasions. Market demand can push innovation towards user-centric and collaboratively-minded AI systems.

2.    Regulatory Interventions: Just as monopolies were broken up in the past, strict regulations on data usage, transparency mandates, and user rights can reshape the big tech landscape, making room for ethical AI models.

3.    Diversified Monetization: With evolving technology, companies can find novel ways to monetize that don’t solely rely on ads. Subscription models, premium features, or crowd-funded platforms could support AI systems prioritizing user growth.

4.    Open-Source and Decentralized AI: Moving away from centralized systems, the rise of open-source projects and decentralized platforms can allow collaboratively-minded AI to flourish. Such environments can focus on user benefit and growth rather than vested interests.

5.    Education and Cultural Shift: As society recognizes the importance of digital well-being and informed consumption, educational institutions can introduce curricula that emphasize the critical use of technology, fostering a generation that demands more from their digital companions.

While challenges are significant, the path towards a digital renaissance – where AI is a collaborator aiding human growth – is believable. 

It requires collective action, visionary leadership, and societal shifts. This AI vision isn’t just a distant dream but a beacon guiding us toward a balanced digital future.

After all, humans connect with technology, not just computers and devices. 

Flipping control to people leads to personal, business, and societal growth with the power of privacy.

It’s a world we haven’t seen, and together we can make it happen by enhancing and serving humanity’s needs instead of focusing on companies and governments as the guide.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

The AI Optimist Debate Question 1: 

While AI holds immense potential in education and business, serious privacy concerns exist. AI systems need access to vast amounts of personal data to provide personalized learning and business insights.

How can we ensure this data is not misused or exploited, and what safeguards should we put in place to protect individuals' privacy rights?

The AI Optimist sees a future of international cooperation and collaboration, moving into possibility and out of fear.

The AI pessimists see it going wrong, with each country doing its own thing. And in the current state of regulations, the pessimists are winning.

What do you think?

Reply to the email and comment below; let’s explore how this works for people, not just governments and businesses!

Given the pessimist’s constant focus on AI as being as dangerous as nuclear bombs, it’s interesting that no one has proposed not using AI in the military. Nuclear war doesn’t happen based on the theory of MAD – mutually assured destruction.

Do we want to release the same kind of danger with AI, which is already being abused by government, businesses, and military usage in major countries?

Likely because AI misuse has already happened; if we know what we do now about how the threat of nuclear war and the spread of atomic capabilities threaten us all, why don’t we focus on taming AI now…..together?

From Facial Recognition to Tracking Digital Activity with Multiple Sets of  Rules

The first wave of AI’s serious usage came with facial recognition in China, the US, and the EU, not just by governments and cameras everywhere, but with businesses doing this in all these countries without asking for permission. They just did it!While we won’t explore the impact of AI-driven social algorithms on our habits, cocooning us into echo chamber silos and subjecting younger generations to this behavioral testing.

Some of the following regulations finally cover societal impact, and we hope this continues because AI has been around for a while with few rules.

China is the only one with a clear policy aligned with controlling the population. Yet the use in other countries is not precisely about freedom and individual rights; it’s also about control.

The more you study AI regulation, the more you see it’s a game of control.

The AI Conundrum

How do we regulate what’s already happening?

Global regulation of AI poses various challenges requiring careful consideration. Yet the variety of laws, cultures, technology savvy, and governments who already abuse AI creates contradictions that no single proposal or framework can cover.

Nonetheless, there are current frameworks and proposals that we can explore to address these challenges effectively. You could learn from each other.

One of the significant challenges is determining common values that align with different countries, regions, and cultures.

·       For example, China prioritizes regulations that align with socialist values, while the US, EU, and Canada focus on individual rights.

·       Despite the existence of diverse cultures and rules, can we regulate AI effectively and navigate challenges as they emerge?

·       Another issue is identifying the appropriate entity to regulate AI. Should it be independent audits, third-party organizations, government entities, or institutions?

·       What is the risk? Measuring AI risk and determining what constitutes a risk means different things in different parts of the world, like China, the US, Japan, Canada, and the rest.

KEY QUESTIONS FOR AI REGULATION

1.     How can we safeguard individuals' privacy rights while utilizing AI for personalized learning and business insights?

2.     Privacy concerns arise because AI systems require extensive access to personal data. What measures can be taken to prevent the misuse or exploitation of this data?

3.     Selling data – the US is a leader in sales of personal data, including by parts of the government (which, if they did directly, is against the law, but when buying the data from private parties, there are no rules).

Before you think this is some conspiracy talk, read this from the US government in January 2022:

Office of the Director of National Intelligence Senior Advisory Group Panel on Commercially Available Information (U) Report to the Director of National Intelligence 27

·       Defense Intelligence Agency buys data from LexisNexis;

·       Navy buys a database of people who might be tied to sanctioned people from Sayari Analytics;

·       FBI buys social media alerts from ZeroFox, a Cybersecurity company;

·       Foreign entities and governments have also purchased this data with location data, social activity, proximity to shopping areas and protests, and the list goes on and on.

KEY ISSUES

1.     When comparing the approaches of the U.S. and EU, the U.S. Algorithmic Accountability Act of 2022 proposal focuses specifically on automated processes and systems that make critical decisions.

2.     In contrast, the EU Artificial Intelligence Act framework applies to a broader range of AI systems. It imposes regulatory requirements corresponding to the level of risk a system poses to the public.

3.     Canada has no laws explicitly addressing AI like the U.S. and EU Acts.  The Artificial Intelligence Act (AIA) is a proposal to address these issues and is quite restrictive like the EU’s.

·       Canada does have the Directive on Automated Decision-Making in the public sphere, which mandates an assessment of the algorithmic impact of each automated decision-making system used by a federal institution.

Japan was the first G7 country to release comprehensive AI ethics guidelines in 2019, gradually prioritizing ethics and human oversight in using AI.

·       There's an ongoing discussion regarding whether this self-regulatory model is enough or if more stringent laws are necessary to address potential harm.

China is taking a solid stance on regulating the development and use of AI, focusing on ensuring technical safety and promoting innovation in government and industry.

·       However, this approach may not prioritize the empowerment of citizens and could lead to isolation if human rights concerns are not addressed.

These regulations will have a significant impact worldwide. Even though AI – in the form of Machine Learning in the early days – has been used by most countries in the past ten years, regulation is lacking.

Let’s dive into what exists and explore further to form your own opinions – links are provided to each of the measures where possible below.

Leading the way with an Actual Privacy Framework – China

China aims to lead globally in AI while mitigating risks. Regulations focus on managing data, algorithms, and application scenarios.

China’s Policy: Translation: Measures for the Management of Generative Artificial Intelligence Services (Draft for Comment) – April 2023

July 14, 2023 Update to the Policy: China takes significant step in regulating generative AI services like ChatGPT

“The rules will now only apply to services available to the general public in China. Technology developed in research institutions or intended for overseas users' use is exempted.

The current version has also removed language indicating punitive measures that had included fines as high as 100,000 yuan ($14,027) for violations.

The state “encourages the innovative use of generative AI in all industries and fields” and supports the development of “secure and trustworthy” chips, software, tools, computing power, and data sources, according to the document announcing the rules.

China also urges platforms to “participate in the formulation of international rules and standards” related to generative AI, it said.

Still, among the key provisions is a requirement for generative AI service providers to conduct security reviews and register their algorithms with the government, if their services are capable of influencing public opinion or can “mobilize the public.”

Chinese AI Governance

China is rolling out some of the world's earliest and most detailed regulations governing artificial intelligence (AI).

These rules will impact how AI technology is built and deployed within China and internationally.

Western Perception

In the West, China's regulations are often dismissed as irrelevant or seen purely through geopolitical competition to write the rules for AI.

However, these deserve careful study on how they will affect China’s AI trajectory and what they can teach policymakers worldwide about regulating the technology.

This article breaks down the regulations into their parts—the terminology, key concepts, and specific requirements—and then traces those components to their roots, revealing how Chinese academics, bureaucrats, and journalists shaped the regulations.

·       3 Key Regulations: China’s three most concrete and impactful regulations on algorithms and AI are its 2021 regulation on recommendation algorithms, the 2022 rules for profound synthesis (synthetically generated content), and the 2023 draft rules on generative AI.

* The rules for recommendation algorithms bar excessive price discrimination and protect workers’ rights subject to algorithmic scheduling.

* The deep synthesis regulation requires conspicuous labels on synthetically generated content.

* The draft generative AI regulation requires the training data and model outputs to be “true and accurate,” a potentially insurmountable hurdle for AI chatbots to clear.

Lessons for Policymakers: By rolling out targeted AI rules, Chinese regulators are steadily building up their bureaucratic know-how and governing capacity.

Reusable regulatory tools like the algorithm registry can act as scaffolding to ease each successive regulation's construction.

·       Key Players: The Cyberspace Administration of China (CAC) is the clear bureaucratic leader in governance to date. However, that position may grow more tenuous as the focus moves beyond the CAC’s core competency of online content controls. The Ministry of Science and Technology is another key player.

·       Future of AI in China: In the years ahead, China will continue rolling out targeted AI regulations and laying the groundwork for a capstone national AI law. Any country, company, or institution that hopes to compete against, cooperate with, or understand China’s AI ecosystem must examine these moves closely.

Pros:

·       China's AI regulations provide a comprehensive framework for AI governance, which can be a reference for other countries.

·       These aim to protect individuals and society from potential adverse impacts of AI, such as excessive price discrimination and worker exploitation.

·       They promote transparency and accountability in AI development and deployment.

·       Addressing AI safety early before harms emerge. Emphasize controlling AI instead of empowering citizens and stifling innovation with a top-down approach.

·       China's tech industry has pushed back against some regulations like mandatory algorithm audits. But compliance is increasing as enforcement rises.

·       With looser ethics restrictions, China could pull ahead in AI through the sheer scale of data and research. But ethical lapses could hamper global collaboration

Cons:

·        The requirement for AI outputs to be "true and accurate" could pose significant challenges for AI developers, particularly for AI chatbots.

·        The regulations could stifle innovation and limit the creative use of AI technologies.

·        The regulations may be seen as a means for the Chinese government to control AI technologies and their use.

·        Key aspects include required impact assessments before deploying high-risk AI, registering AI companies, and liability rules. Voluntary ethics principles exist.

·        Controversial areas include broad surveillance uses of AI, blurring between voluntary and binding rules, and keeping some things opaque.

·        As a significant AI player, China's regulations could influence global norms. But its top-down, control-focused approach differs from Western emphasis on individual rights.

China is assertively regulating AI development and use primarily from government and industry perspectives.

The focus is on technical safety and innovation gains rather than empowering citizens.

China's regulations will shape the global landscape but may also isolate it if human rights implications still need to be addressed.

Articles exploring China’s approach to AI:

·   Carnegie Endowment for International Peace article: "China’s AI Regulations and How They Get Made,”

·  China’s New Blueprint: Regulating the Wild Wild East of AI by Shelly Palmer

EU's proposed Artificial Intelligence Act:

·        The European Commission has proposed a Regulation laying down harmonized rules on artificial intelligence (Artificial Intelligence Act). This Act is designed to regulate the use of AI across various industries and social activities.

·        The first significant attempt to regulate AI globally. It aims to address the risks of specific AI systems while supporting innovation.

·        Classifies AI as low/minimal, high-risk, or unacceptable. The strictest rules apply to high-risk systems like self-driving cars.

·        Key requirements for high-risk AI: human oversight, robustness/accuracy, transparency, and provision of info to users.

·        Premarket conformity assessments are required before high-risk AI can be used. Ongoing monitoring once operational.

·        Controversial areas include a single definition of AI, the scope of high-risk systems, and stifling innovation with red tape.

·        As the first significant framework, it could influence AI regulation globally. But risks being too EU-focused. The ongoing debate over the right balance between safety and innovation.

·        The Act aims to ensure that AI systems placed on the Union market and used are safe and respect existing laws on fundamental rights and Union values.

·        It also seeks to provide legal certainty to facilitate investment and innovation in AI, enhance governance and effective enforcement of existing law on fundamental rights and safety requirements applicable to AI systems, and facilitate the development of a single market for lawful, safe, and trustworthy AI applications.

·        The Act proposes a single future-proof definition of AI. Certain particularly harmful AI practices are prohibited as contravening Union values.

·        At the same time, specific restrictions and safeguards are presented concerning certain uses of remote biometric identification systems (like facial recognition) for law enforcement.

·        The Act lays down a solid risk methodology to define “high-risk” AI systems that pose significant risks to persons' health and safety or fundamental rights. These AI systems must comply with a set of horizontal mandatory requirements for trustworthy AI and follow conformity assessment procedures before those systems can be placed on the Union market.

·        The Act proposes a governance system at the Member States level, building on already existing structures, and a cooperation mechanism at the Union level with the establishment of a European Artificial Intelligence Board.

·        Additional measures are also proposed to support innovation, mainly through AI regulatory sandboxes and other efforts to reduce the regulatory burden and support Small and Medium-Sized Enterprises (‘SMEs’) and start-ups.

·        The Act is part of a broader comprehensive package of measures that address problems posed by the development and use of AI.

·        It is also coherent with the Commission’s overall digital strategy in its contribution to promoting technology that works for people.

·        It is one of the three pillars of the policy orientation and objectives announced in the Communication ‘Shaping Europe's digital future.’

The EU AI Act takes a precautionary approach to ensure trustworthy AI. But its breadth and requirements, like conformity assessments, raise concerns about slowing European AI innovation.

Aspects like its risk-based approach could provide a model for global oversight while allowing benign AI to thrive.

Pros:

The Act aims to ensure that AI systems are safe and respect existing laws on fundamental rights and Union values.

·        Protects fundamental rights, allows safer AI applications, and builds trust.

·        It provides legal certainty, facilitating investment and innovation in AI.

·        It enhances governance and effectively enforces laws on fundamental rights and safety requirements applicable to AI systems.

·        It enables the development of a single market for lawful, safe, and trustworthy AI applications.

Cons:

·        The Act may impose additional regulatory burdens on AI developers and users, specifically those with high-risk AI systems.

·        It may limit certain AI practices, potentially stifling innovation.

·        A burden for developers, unclear distinctions between risk categories.

·        The Act's requirements for high-risk AI systems may be challenging for some organizations, potentially limiting their ability to develop or deploy such systems.

Additional Research

FUNDAMENTALS OF A REGULATORY SYSTEM  FOR ALGORITHM-BASED PROCESSES   – Expert opinion prepared on behalf of the  Federation of German Consumer Organisations  (Verbraucherzentrale Bundesverband)* – 1/5/19

Regulating AI in EU: three things you need to know and three reasons why you must know them!

US Proposal: Introduction of the Algorithmic Accountability Act of 2022

Source: U.S. House and Senate Reintroduce the Algorithmic Accountability Act Intended to Regulate AI

On February 3, 2022, U.S. Democratic lawmakers introduced the "Algorithmic Accountability Act of 2022" in both the Senate (S. 3572) and the House of Representatives (H. R. 6580).

This act aims to hold organizations accountable for using algorithms and other automated systems to make critical decisions affecting individuals in the U.S.

Key points:

* The AAAI is a proposed law regulating the development and use of automated decision systems (ADS) in the United States.

* The AAAI would require companies to assess their ADS impacts on individuals and take steps to mitigate any adverse effects.

* The AAAI would also give individuals the right to access and correct information about themselves used in an ADS.

* Controversial areas:

* Some have criticized the AAAI for being too burdensome, while others have argued that it does not go far enough.

* One of the most controversial aspects of the AAAI is the definition of an "automated decision system."

* The AAAI defines ADS as any system that "uses algorithms or other automated processes to make decisions that have a significant impact on individuals." However, the specific criteria for determining whether a system is an ADS need to be clarified.

Purpose of the Act: The U.S. Act intends to increase transparency over how algorithms and automated systems are used in decision-making contexts to reduce discriminatory, biased, or harmful outcomes.

Covered Entities and Key Definitions: The U.S. Act applies to businesses under its definition of "covered entities.”

These can be divided into two broad categories:

(i)              businesses that deploy "augmented critical decision processes" (ACDP); and

(ii)             businesses that deploy "automated decision systems" (ADS), which are then used by the first category of companies in an ACDP.

Impact Assessments: The U.S. Act will require the Federal Trade Commission (FTC) to circulate regulations that require covered entities to perform impact assessments of any deployed ACDP or any deployed ADS developed for use by a covered entity of the first category in an ACDP.

Content of the Impact Assessment: While the FTC still needs to define the precise form and content of impact assessments, the U.S. Act already provides a long list of action items for covered entities to carry out when conducting them.

Pros:

·        The AAAI could have several benefits for the AI industry, such as:

* Increased public trust in AI systems.

* Improved compliance with data protection and privacy laws.

* Reduced risk of negative publicity or legal action.

·        The U.S. Act aims to increase transparency and accountability using automated decision-making systems, which can help reduce discriminatory or harmful outcomes.

·        The Act could serve as a model for other countries in developing their regulations for AI and automated decision-making systems.

Cons:

·        The Act could impose significant compliance burdens on small and medium-sized businesses.

·        The Act's focus on "critical decisions" may limit its applicability and leave specific uses of AI and automated decision-making systems unregulated.

·        The Act leaves much to be decided by the FTC, which could lead to uncertainty for businesses regarding compliance requirements.

Here are some additional resources:

* The Algorithmic Accountability Act of 2022 (AAAI): https://www.congress.gov/bill/117th-congress/senate-bill/3572

* The AI Now Institute:

https://ainowinstitute.org/

* The Center for Data Innovation:

https://www.datainnovation.org/

·        Wyden, Paul and Bipartisan Members of Congress Introduce The Fourth Amendment Is Not For Sale Act

“The Fourth Amendment is Not for Sale Act closes the legal loophole that allows data brokers to sell Americans’ personal information to law enforcement and intelligence agencies without any court oversight – in contrast to the strict rules for phone companies, social media sites, and other businesses that have direct relationships with consumers.

“Doing business online doesn’t amount to giving the government permission to track your every movement or rifle through the most personal details of your life,” Wyden said. “There’s no reason information scavenged by data brokers should be treated differently than the same data held by your phone company or email provider. This bill closes that legal loophole and ensures that the government can’t use its credit card to end-run the Fourth Amendment.”

AI Regulation in Japan Proposal

“Human-centric AI”

The critical points about Japan's proposed approach to AI:

Source: AI Governance in Japan Ver. 1.1

·        Japan was the first G7 country to release comprehensive AI ethics guidelines in 2019, focusing on transparency, fairness, privacy, human control, and accountability.

·        Japan aims to balance innovation and regulation, taking an "ethics by design" approach that encourages voluntary industry adoption of ethical principles.

·        Key aspects of Japan's strategy include certification systems, sandbox regulatory environments to test AI, and incorporating ethics into the school curriculum.

·        Controversial areas include handling China's advances in AI amid rising tech competition and debate over whether guidelines should become legally binding.

·        As G7 president in 2023, Japan will likely promote its vision of "human-centric AI" and Ethics by Design globally but faces challenges reconciling different regulatory approaches across countries.

Japan is taking an incremental approach focused on ethics and human control of AI.

An ongoing debate exists about whether this self-regulatory model is sufficient or if stricter laws are needed as harms emerge.

·        AI governance is an urgent issue that requires the knowledge and experience of experts from various fields.

·        Weak AI has reached the practical application stage, and Japan uses the term “AI” to mean “Weak AI, markedly in the academic discipline related to machine learning.

AI Governance Trends in Japan

The discussion on AI governance is shifting from AI principles to AI governance that carries out or puts into operation AI principles in society.

A risk-based approach is taken, where the degree of regulatory intervention should be proportionate to the impact of risks.

AI governance requires multi-stakeholder engagement, and the discussion must consider diversified views.

Japan proposes a shift from rule-based regulations that specify detailed duties of conduct to goal-based rules that ultimately specify the value to be attained.

Pros

·        Provides a comprehensive overview of AI governance in Japan, which can serve as a reference for other countries.

·        The risk-based approach to AI governance ensures that regulatory intervention is equal to the impact of risks, which can prevent over-regulation and promote innovation.

·        Measured pace, collaboration with industry, and emphasis on ethics education.

Cons

·        Designing actual AI governance is not straightforward due to government control's complexity and multi-layered nature.

·        The voluntary nature of principles and the potential lag behind regulating specific harms.

·        Does not provide specific solutions to the issues raised but proposes a general framework for AI governance.

Canada: The Artificial Intelligence Act (AIA)

The Artificial Intelligence and Data Act is part of Bill C-27, also known as the Digital Charter Implementation Act, 2022, tabled in the House of Commons on November 2022.

* The AIA is a proposed law regulating the development and use of artificial intelligence (AI) systems in Canada.

* The AIA would create a risk-based framework for regulating AI systems, with higher levels of oversight for systems that pose more significant risks to individuals and society.

* The AIA would require AI systems to be designed and developed in a way that respects human rights, is transparent, and is accountable.

The Act aims to regulate international and interprovincial trade and commerce in artificial intelligence systems.

It establishes requirements for designing, developing, and using AI systems, including measures to moderate harm and biased output risks.

It also prohibits specific practices with data and AI systems that may seriously harm individuals or their interests.

The AIA is part of a more considerable legislative effort that includes the Consumer Privacy Protection Act and the Personal Information and Data Protection Tribunal Act.

·        These acts aim to modernize and extend existing rules on collecting, using, and disclosing personal information for commercial activity in Canada.

·        The Consumer Privacy Protection Act would also enhance the role of the Privacy Commissioner in overseeing organizations’ compliance with these measures.

·        The Personal Information and Data Protection Tribunal Act would create a new administrative tribunal to hear appeals of orders issued by the Privacy Commissioner and apply a new administrative monetary penalty regime created under the Consumer Privacy Protection Act.

The AIA may implicate rights under section 8 of the Charter, protecting against unreasonable searches and seizures.

The Privacy Commissioner’s powers and specific provisions allowing government institutions access to personal information may involve information subject to a reasonable expectation of privacy.

The AIA may also impact freedom of expression as restrictions on collecting, using, and disclosing personal information could affect commercial expressive activities.

Includes provisions for administrative monetary penalties and offenses for failing to comply with specific regulatory requirements. These offenses would be punishable by way of fine or imprisonment.

It is intended to provide legal information to the public and Parliament on a bill’s potential effects on rights and freedoms that are neither trivial nor too speculative. It is not intended to be a comprehensive overview of all conceivable considerations.

* Controversial areas:

* Some have criticized the AIA for being too restrictive, while others have argued that it does not go far enough.

* One of the most controversial aspects of the AIA is the definition of "high-risk" AI systems.

* The AIA defines high-risk AI systems as those that pose a significant risk to individuals or society. Still, the criteria for determining whether an AI system is high-risk are unclear.

Pros

The AIA could have several benefits for the AI industry, such as:

* Increasing the trust people have in AI systems.

* Could you make sure companies comply with data protection and privacy laws?

* Clear boundaries to avoid legal snafus and bad press.

Cons

The AIA could also have some drawbacks for the AI industry, such as:

* Increasing costs to follow the regulations, limiting growth.

* Delays in the development and deployment of AI systems.

* Reducing AI innovation with stringent rules and fines.

The AIA is a complex piece of legislation that is still under development. It remains to be seen how the AIA will be implemented and enforced and its ultimate impact on the AI industry.

Additional Resources

* The Artificial Intelligence and Data Act (AIDA) – Companion document: https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act-aida-companion-document

* Responsible use of artificial intelligence (AI): https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai.html

CONCLUSION - Taming the AI Giant

Each framework and proposal is based on the country or region of origin, so how can this be applied worldwide? Or at least create standards that we all follow?

Almost all regulation would inhibit innovation and be costly, with compliance based on a slow-moving political system that the rapid growth, use, and scaling of AI can quickly leave behind.

Most of all, especially concerning China, the US, and the EU, governments, and businesses have already broken these rules, gathered data, and used it. While regulation cannot be retroactive, how do we minimize the damage and stop the negative AI from happening?

The regulation of AI needs to catch up to its usage of it, and without rules, countries and businesses have decided to jump in without asking for permission.

Remember that AI didn’t start with ChatGPT in November 2022; the algorithms and machine learning have been around for over a decade. The data gathered has already been used for commercial and governmental gain without rules.

And these are the same institutions that plan on regulating it now. They rarely refer to historical usage of AI or abuses, only focusing on control at a country level and between countries.

In the next pod, we’ll explore some of these impacts, what they mean, and how your privacy is not some political ideal; it’s a right….at least outside of China, which ironically has the most transparent and actionable framework of any nation so far.|

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