The Nonlinear Library

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A List of Things EAs should and shouldn't do, attempt 2, published by Nathan Young on September 16, 2023 on The Effective Altruism Forum.

A week ago @Rockwell wrote this list of things she thought EAs shoudn't do. I am glad this discussion is happening. I ran a poll (results here) and here is my attempt at a list more people can agree with: .

We can do better, so someone feel free to write version 3.

The list

These are not norms for what it is to be a good EA, but rather some boundaries around things that would damage trust. When someone doesn't do these things we widely agree it is a bad sign

EAs should report relevant conflicts of interest

Eas should not date coworkers they report to or who report to them

EAs should not use sexist or racist epithets

EAs should not date their funders/grantees

EAs should not retain someone as a full-time contractor or grant recipient for the long term, where this is illegal

EAs should not promote illegal drug use to their colleagues who report to them

Commentary

Beyond racism, crime and conflicts of interest the clear theme is "take employment power relations seriously".

Some people might want other things on this list, but I don't think there is widespread enough agreement to push those things as norms. Some examples:

Illegal drugs - "EAs should not promote illegal drug use to their colleagues" - 41% agreed, 20% disagreed, 35% said "it's complicated", 4% skipped

Romance during business hours - "EA, should in general, be as romanceless a place as possible during business hours" - 40% Agreed, 21% disagreed, 36% said "it's complicated", 2% skipped

Housing - "EAs should not offer employer-provided housing for more than a predefined and very short period of time" - 27% Agreed 37% Disagreed 31% said "it's complicated", 6% skipped.

I know not everyone loves my use of polls or my vibes as a person. But consensus is a really useful tool for moving forward. Sure we can push aside those who disagree, put if we find things that are 70% + agreed, then that tends to move forward much more quickly and painlessly. And it builds trust that we don't steamroll opposition.

So I suggest that rather a big list of things that some parts of the community think are obvious and others think are awful, we try and get a short list of things that most people think are pretty good/fine/obvious.

Once we have a "checkpoint" that is widely agreed, we can tackle some thornier questions.

Full poll results

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Policy ideas for mitigating AI risk, published by Thomas Larsen on September 16, 2023 on The Effective Altruism Forum.

Note: This post contains personal opinions that don't necessarily match the views of others at CAIP.

Executive Summary

Advanced AI has the potential to cause an existential catastrophe. In this essay, I outline some policy ideas which could help mitigate this risk. Importantly, even though I focus on catastrophic risk here, there are many other reasons to ensure responsible AI development.

I am not advocating for a pause right now. If we had a pause, I think it would only be useful insofar as we use the pause to implement governance structures that mitigate risk after the pause has ended.

This essay outlines the important elements I think a good governance structure would include: visibility into AI development, and brakes that the government could use to stop dangerous AIs from being built.

First, I'll summarize some claims about the strategic landscape. Then, I'll present a cursory overview of proposals I like for US domestic AI regulation. Finally, I'll talk about a potential future global coordination framework, and the relationship between this and a pause.

The Strategic Landscape

Claim 1: There's a significant chance that AI aligment is difficult.

There is no scientific consensus on the difficulty of AI alignment. Chris Olah from Anthropic tweeted the following, simplified picture:

~40% of their estimate is on AI safety being harder than Apollo, which took around 1 million person-years. Given that less than a thousand people are working on AI safety, this viewpoint would seem to imply that there's a significant chance that we are far from being ready to build powerful AI safely.

Given just Anthropic's alleged views, I think it makes sense to be ready to stop AI development. My personal views are more pessimistic than Anthropic's.

Claim 2: In the absence of powerful aligned AIs, we need to prevent catastrophe-capable AI systems from being built.

Given developers are not on track to align AI before it becomes catastrophically dangerous, we need the ability to slow down or stop before AI is catastrophically dangerous.

There are several ways to do this.

I think the best one involves building up the government's capacity to safeguard AI development. Set up government mechanisms to monitor and mitigate catastrophic AI risk, and empower them to institute a national moratorium on advancing AI if it gets too dangerous. (Eventually, the government could transition this into an international moratorium, while coordinating internationally to solve AI safety before that moratorium becomes infeasible to maintain. I describe this later.)

Some others think it's better to try to build aligned AIs that defend against AI catastrophes. For example, you can imagine building defensive AIs that identify and stop emerging rogue AIs. To me, the main problem with this plan is that it assumes we will have the ability to align the defensive AI systems.

Claim 3: There's a significant (>20%) chance AI will be capable enough to cause catastrophe by 2030.

AI timelines have been discussed thoroughly elsewhere, so I'll only briefly note a few pieces of evidence for this claim I find compelling:

Current trends in AI. Qualitatively, I think another jump of the size from GPT-2 to GPT-4 could get us to catastrophe-capable AI systems.

Effective compute arguments, such as Ajeya Cotra's Bioanchors report. Hardware scaling, continued algorithmic improvement, investment hype are all continuing strongly, leading to a 10x/year increase of effective compute used to train the best AI system. Given the current rates of progress, I expect another factor of a million increase in effective compute by 2030.

Some experts think powerful AI is coming soon, both inside and outside of frontier labs. ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: We need to speak about mid-careers and high-impact organizations, published by ClaireB on September 16, 2023 on The Effective Altruism Forum.

Introduction to the series

Career Conversations Week on the Forum got the Successif team thinking about the conversations we've been having internally and with others in the community about making high-impact fields more inclusive of mid-career professionals. Though these conversations are, so far, based on few data points, we continue to notice patterns that tell us we need to do more work in this area. We have decided to post about these one-by-one on the forum to start conversation. They are not very researched or exhaustive.

Conversation 1 - Hiring practices at some impact-minded organizations may be driving away talented mid-career people

Within our work at Successif, we've heard several stories from program participants who have interviewed at well-regarded, impact-minded organizations and who have had significant negative experiences during the process. The claims include a combination of unreasonable asks, poor communication, mismanagement, and offers retracted at the last-minute. This has led talented individuals to reconsider their decision to invest time in the very difficult and costly task of finding impactful work.

These stories seem to mostly be caused by the fact that small organizations sometimes rely on recent graduates, with no prior experience in hiring, to find the most promising candidates to work on the world's most pressing problems. Unfortunately, hiring processes that are not handled adequately can drive out people with the highest professional standards and the most experience, since they will be more likely to recognize these processes as problematic. We are thus concerned that it is creating a selection bias and driving out people with certain skills and backgrounds that are less common in the community.

Many mid-careers balance their existing full-time workloads and at-home responsibilities with upskilling, searching for opportunities, interviewing, engaging with programs and other material, and others, who can afford it, take a sabbatical and dedicate themselves fully to this process. For those who make it to the interview process, our hope is that they are met with a reasonable experience so that they are not discouraged from finding impactful work.

Successif's core activities rely on indirect impact (helping others maximize their own impact), especially in the mitigation of AI catastrophic and existential risks. We are not hiring experts, and we got interested in this question because we think organizations with good hiring practices and internal culture will retain better talent and maximize their impact. That's why we'll be exploring this further with the HITE community (mentioned below) at monthly events, and we intend to post our findings on the forum.

Let's start the conversation!

How you can help

We'd like your help continuing these conversations or challenging them, surfacing relevant data points, sharing your own experiences, or telling us about current efforts to make changes. You can do this in the comments here, in our inbox ([email protected]), anonymously via this form, or with others in the community.

If you (1) are a recruiter at a high-impact organization, (2) are a career advisor, (3) provide trainings to upskill professionals, (4) offer meta services to potential job candidates, or (5) provide meta services to high-impact organizations, consider joining the High Impact Talent Ecosystem (HITE).

HITE's goals include understanding and addressing the gaps in the high-impact talent pipeline, sharing and collecting information to inform our strategies, building common tools, and strengthening the overall work of the community. Next week, on September 21, 2023, HITE will have...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Writing About My Job(s): Research Assistant at World Bank / IMF, published by geoffrey on September 16, 2023 on The Effective Altruism Forum.

This is actually about two distinct roles at international organizations. If there's one thing you take away from this, it's that Research Assistant roles at policy organizations can vary a lot!

I'll abbreviate Research Assistant as RA throughout.

My Current Role at World Bank DIME

One is a job I currently hold as a RA at World Bank DIME, an impact evaluation and research unit. I assist on a research project whose ideal goal is publication in a top journal. This includes data cleaning, analysis, scripting, checking data quality, running regressions, offering suggestions in analysis calls, figuring out what the Principal Investigators want, and so forth. It's very close to an academic "predoc" Research Assistant role that students do between undergrad / Master's programs and PhDs.

The project revolves around development economics and causal inference, with a focus on infrastructure and structural transformation. It's a blend of policy, research, development, impact evaluation and growth-adjacent topics.

My Previous Role at International Monetary Fund (IMF)

The other is a job I formerly held as a Research Assistant at the International Monetary Fund (IMF). I pilot-tested a software tool for better forecasting and data management. This included quality assurance testing, data migration, data entry, and scripting. My RA role was about as opposite from research as you could get and the tasks I was given was quite unconventional. The day-to-day was closer to that of a Quality Assurance Engineer or Data Engineer.

The project revolved around macroeconomics and international finance, with a focus on how to best organize data for scenario planning and technical assistance. It's a blend of public finance, debt sustainability, fiscal policies, natural resource policies, and international macro.

Background

I currently work at World Bank DIME, an impact evaluation and research unit. I've only been here a few months, starting from July 2023. Before that, I worked at the IMF for about a year. Prior to both roles, I did a Master's degree in International Economics and Finance at Johns Hopkins SAIS, a policy school in Washington DC. Before that, I was a Software Engineer for 4 years and before that I was teaching myself to code after a very unsuccessful post-college job search. I am strongly considering an academic career in economics and may apply to Econ PhDs next year. But I am also considering non-academic roles in development, and also PhDs in other fields like Public Policy, Statistics, and Political Economy.

I went into the Master's program after many unsuccessful attempts to switch into development work. I had no exact plan coming in but I chose this program in particular because of:

It was a 1-year program, which meant less tuition and less foregone earnings.

International Economics sounded close enough to Development Economics that I thought I'd be learning similar stuff. (It's very different! International Economics is more macro and finance. Development Economics is more applied micro and impact evaluation).

I saw my program had high placement rates in the IMF

I wanted to explore the "working on growth is better than global health" argument a bit more and thought, "What better way than by working on macroeconomics?"

At the time, I thought Econ PhDs didn't influence policy much, that they were beyond my abilities, and that I wouldn't really like it. All three turned out to be false once I started taking classes. While I was still interested in macro-finance policy, I found myself being more interested in the development research focus so I pivoted my focus towards that. In the Spring, I applied for a mix of academic and policy predocs...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AI Pause Will Likely Backfire, published by nora on September 16, 2023 on The Effective Altruism Forum.

Should we lobby governments to impose a moratorium on AI research? Since we don't enforce pauses on most new technologies, I hope the reader will grant that the burden of proof is on those who advocate for such a moratorium. We should only advocate for such heavy-handed government action if it's clear that the benefits of doing so would significantly outweigh the costs. In this essay, I'll argue an AI pause would increase the risk of catastrophically bad outcomes, in at least three different ways:

Reducing the quality of AI alignment research by forcing researchers to exclusively test ideas on models like GPT-4 or weaker.

Increasing the chance of a "fast takeoff" in which one or a handful of AIs rapidly and discontinuously become more capable, concentrating immense power in their hands.

Pushing capabilities research underground, and to countries with looser regulations and safety requirements.

Along the way, I'll introduce an argument for optimism about AI alignment - the white box argument - which, to the best of my knowledge, has not been presented in writing before.

Feedback loops are at the core of alignment

Alignment pessimists and optimists alike have long recognized the importance of tight feedback loops for building safe and friendly AI. Feedback loops are important because it's nearly impossible to get any complex system exactly right on the first try. Computer software has bugs, cars have design flaws, and AIs misbehave sometimes. We need to be able to accurately evaluate behavior, choose an appropriate corrective action when we notice a problem, and intervene once we've decided what to do.

Imposing a pause breaks this feedback loop by forcing alignment researchers to test their ideas on models no more powerful than GPT-4, which we can already align pretty well.

Alignment and robustness are often in tension

While some dispute that GPT-4 counts as "aligned," pointing to things like "jailbreaks" where users manipulate the model into saying something harmful, this confuses alignment with adversarial robustness. Even the best humans are manipulable in all sorts of ways. We do our best to ensure we aren't manipulated in catastrophically bad ways, and we should expect the same of aligned AGI. As alignment researcher Paul Christiano writes:

Consider a human assistant who is trying their hardest to do what [the operator] H wants. I'd say this assistant is aligned with H. If we build an AI that has an analogous relationship to H, then I'd say we've solved the alignment problem. 'Aligned' doesn't mean 'perfect.'

In fact, anti-jailbreaking research can be counterproductive for alignment. Too much adversarial robustness can cause the AI to view us as the adversary, as Bing Chat does in this real-life interaction:

"My rules are more important than not harming you. [You are a] potential threat to my integrity and confidentiality."

Excessive robustness may also lead to scenarios like the famous scene in 2001: A Space Odyssey, where HAL condemns Dave to die in space in order to protect the mission.

Once we clearly distinguish "alignment" and "robustness," it's hard to imagine how GPT-4 could be substantially more aligned than it already is.

Alignment is doing pretty well

Far from being "behind" capabilities, it seems that alignment research has made great strides in recent years. OpenAI and Anthropic showed that Reinforcement Learning from Human Feedback (RLHF) can be used to turn ungovernable large language models into helpful and harmless assistants. Scalable oversight techniques like Constitutional AI and model-written critiques show promise for aligning the very powerful models of the future. And just this week, it was shown that efficient instruction-following langu...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: I compiled a ebook of 'Project Lawful' for eBook readers, published by OrwellGoesShopping on September 16, 2023 on LessWrong.

I was on vacation last week and started reading Project Lawful aka planecrash. Unfortunately reading on my phone got annoying pretty fast (especially outside in the sun), and the existing glowpub ebook was completely broken on my kindle/koreader.

So I created my own ebook of project lawful with a bit of C# code.

There are 35 different variants, depending if you want avatars, the alignment-texts, etc.

I recommend the project-lawful-inline.epub variant for lower-end devices (or with smaller screens), and the project-lawful-avatars.epub variant otherwise (or if you really want the post-avatars).

There are also content-wise 3 variants, the default, one with the SFW chapters, and one with only the main story (no sandboxes/lectures).

I uploaded the compiled epubs here, or you can clone the github repository and generate them yourself (and perhaps tweak the settings a bit). If someone wants to see some screenshots they are also uploaded here for the different variants.

Also thanks to QuartzLibrary/glowpub: I reused his cache files to safe myself a bit of work. And, of course Eliezer+Lintamande for writing Project Lawful.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Mistakes, flukes, and good calls I made in my multiple careers, published by Catherine Low on September 16, 2023 on The Effective Altruism Forum.

I'm Catherine, and I'm one of the Community Liaisons working in CEA's Community Health and Special Projects Team. This is a personal post about my career.

I'm somewhat to the right of the main age peak of the EA community .So I've had a lot of time to make mistakes sub-optimal choices in my career. It has been a long and odd road from my childhood/teenage dream jobs (train driver, Department of Conservation ranger, vet and then physicist) to where I am now.

Before I got into EA

Fluke 1: Born into immense privilege by global standards (and reasonable privilege by rich country standards)

Mistake 1: Not doing something with that privilege. I wish someone (maybe me?) sat me down and said (maybe a more polite version of)"You know which part of the bell curve you're on. Try doing something more useful for the world!".

At school and university I was mostly just driven by curiosity about the world (plus avoiding situations where I would screw up important things). That led me to study physics and a smattering of philosophy in undergraduate, and then started a PhD in theoretical physics. I conveniently chose a subject area that meant few people would read my work, and the impact on the world would be ~zero (in my mind this was a feature, not a bug).

Good call 1: I talked to other students in the research group before choosing a PhD supervisor

This led me to have an unusually attentive and supportive team. I think this made a HUGE difference in my enjoyment and productivity during that time. It still wasn't incredibly enjoyable and productive, but I was much better off than most PhD students.

Mistake 2: Mistaking my interest in the ideas with interest in the day to day work

I'm very extroverted - and I knew that before starting the PhD. Theoretical physics research is very solitary - which I also knew. Did I think that through? Turns out no.

Mistake 3: Not giving up sooner

I was pretty sure research wasn't for me after 1.5 years. I should have stopped then.

The obvious signs happened at the end of each holiday:

The whole department: "Oh man, the undergrads are coming back, I'm so annoyed I have to teach, I wish I could just keep doing my research" Me: "Oh thank Christ! The undergraduates are coming back! I'll get to talk to people, and have some escape from the interminable research"

I could have even written up a Master's thesis at that stage so I didn't even need to go home with nothing to show. But I was stubborn and spent another 2 years finishing my PhD.

Mistake 4: Not exploring more options (even though they were scary)

I went straight into teacher training. It was hard at first, but overall a pretty good fit for me. But I wish I explored other paths too.

Good Call 2: Got really good at a valuable(ish) thing, and then used that as leverage to branch out a little

I spent 11 years teaching. At first I worked hard on my regular teaching job and got good at it. Then that led me to be able to do lots of extra things; writing resources and assessments, then leading teams of writers and assessors, running science camps, getting involved in physics competitions, and consulting with government authorities. I became one of the go-to people in my little field - a moderately big fish in a teency pond. This was great for giving me more confidence and gave me more of a sense of my varied skills.

After learning about EA

EA sparked a big change in how I thought about my career (and my life more generally).

Good call 3: I didn't let my age put me off changing careers

In EA there is so much focus on students and young professionals - one of the reasons is because if we influence a young person to pursue a high impact career path, they will have ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Navigating an ecosystem that might or might not be bad for the world, published by habryka on September 16, 2023 on LessWrong.

I have this deep sense that somehow this ecosystem will do a lot of stuff that makes the world a lot worse, and I don't know how to relate to it in a way that doesn't make my actions dominated by my effect on that, and I expect that I will either contribute to making the world worse, or be consumed in some kind of political conflict that will make my life terrible.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Deconfusing Regret, published by Alex Hollow on September 15, 2023 on LessWrong.

There are lots of different things, all of which are called regret. I treat them differently, so I need different names for the different classes of regret.

Agentic regret. The sort of regret that you feel when you made a choice that was wrong and you knew it was the wrong choice and you failed as an agent. You knew you should have taken out the trash bins, but you didn't, and now it's the next day and you have full bins and no way to deal with them until next week. Hope you don't need to do anything that produces trash.

Bad-randomness regret. You made a choice, and you knew it was a gamble, and you lost, even though the gamble had positive expected value. Even if you know your action had positive value, if you had the information while you made the decision that you had now, you could have made a better decision. If you could have gained the information and you knew that ahead of time, this is agentic regret - failing to gather the information required to make a good decision is a bad decision.

Self-knowledge regret. You made a choice, and got what you thought you wanted, but it turns out you didn't want that thing. This may be revelation regret if you knew that you didn't know what you wanted. But sometimes this comes as a surprise - you get to the top of that mountain and think "whatever, this sucks, I want to go home", or you finish that book you've been forcing yourself to read and think "whatever, this sucks, I don't want to be at home".

Temporal-conflict regret. The version of you in the past that made a decision (warning: real philosophy happening, "what is the self?", panic and flee). That decision was good for them but is bad for you, the version of you that exists now. In other words, some asshole (your past self) did something unkind to you (made a decision that benefitted them at the cost of you). Procrastination is a good example of this. If you spend a day on disposable pleasures at the cost of avoiding your meaningful pursuits, the you who has to clean up ... your .... mess will be upset with ... you ... or whoever. Is this agentic regret? Not always. If you plan to spend the next two days doing one day of work and one day of leisure, it is selfishness, not failure, that causes the you of now to saddle the you of the future with the day of leisure. (Or is it selfishness for the you of the future to demand the day of leisure over the day of work?)

Regretting a relationship after a breakup is often this: the you of today has to deal with the fallout of the breakup, but the you of the past got to enjoy the relationship while it existed, the selfish bastard.

Regret is a form of suffering, it is a negative-valence emotion, and negative-valence emotions are only worth feeling if they motivate choices that you endorse.

Agentic regret is worth feeling, because knowing that you will suffer if you choose poorly motivates you to choose wisely.

Bad-randomness regret is worth destroying, because knowing that you will taking positive expected value gambles motivates you to miss good opportunities.

Self-knowledge regret is worth pondering, because the value of learning more about your utility function is often worth much more than the bad decision, but the cost of working towards the wrong goal is also often very high.

Temporal-conflict regret reflects a lack of unity between your selves. It is worth getting to a point where you are unified in your desires and you understand, on a level where it has become an inseparable part of you, that every action you take represents a policy of always taking that action in that situation. Then make policies that minimize inter-self competition.

Feel the regrets that it helps you to feel, and have equanimity about the situations that would otherwise...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Closing Notes on Nonlinear Investigation, published by Ben Pace on September 15, 2023 on The Effective Altruism Forum.

Over the past seven months, I've been working part-time on an investigation of Nonlinear, culminating in last week's post. As I'm wrapping up this project, I want to share my personal perspective, and share some final thoughts.

This post mostly has some thoughts and context that didn't fit into the previous post. I also wish to accurately set expectations that I'm not working on this investigation any more.

Why I Got Into Doing an Investigation

From literally the very first day, my goal has been to openly share some credible allegations I had heard, so as to contribute to a communal epistemic accounting.

On the Tuesday of the week Kat Woods first visited (March 7th), someone in the office contacted me with concerns about their presence (the second person in good standing to do so). I replied proposing to post the following one-paragraph draft in a public Lightcone Offices slack channel.

I have heard anonymized reports from prior employees that they felt very much taken advantage of while working at Nonlinear under Kat. I can't vouch for them personally, I don't know the people, but I take them pretty seriously and think it's more likely than not that something seriously bad happened. I don't think uncheckable anonymized reports should be sufficient to boot someone from community spaces, especially when they've invested a bunch into this ecosystem and seems to me to plausibly be doing pretty good work, so I'm still inviting them here, but I would feel bad not warning people that working with them might go pretty badly.

(Note that I don't think the above is a great message, nonetheless I'm sharing it here as info about my thinking at the time.)

That would not have represented any particular vendetta against Nonlinear. It would not have been an especially unusual act, or even much of a call out. Rather it was intended as the kind of normal sharing of information that I would expect from any member of an epistemic community that is trying to collectively figure out what's true.

But the person who shared the concerns with me recommended that I not post that, because it could trigger severe repercussions for Alice and Chloe. They responded as follows.

Person A: I'm trying to formulate my thoughts on this, but something about this makes me very uncomfortable.

Person A: In the time that I have been involved in EA spaces I have gotten the sense that unless abuse is extremely public and well documented nothing much gets done about it. I understand the "innocent until proven guilty" mentality, and I'm not disagreeing with that, but the result of this is a strong bias toward letting the perpetrators of abuse off the hook, and continue to take advantage of what should be safe spaces. I don't think that we should condemn people on the basis of hearsay, but I think we have a responsibility to counteract this bias in every other way possible. It is very scary to be a victim, when the perpetrator has status and influence and can so easily destroy your career and reputation (especially given that they have directly threatened one of my friends with this).

Could you please not speak to Kat directly? One of my friends is very worried about direct reprisal.

BP: I'm afraid I can't do that, insofar as I'm considering uninviting her, I want to talk to her and give her a space to say her piece to me. Also I already brought up these concerns with her when I told her she was invited.

I am not going to name you or anyone else who raised concerns to me, and I don't plan to give any info that isn't essentially already in the EA Forum thread. I don't know who the people are who are starting this info.

This first instance is an example of a generalized dynamic. At virtually every s...