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AI Ethics 2025: Navigating Bias, Deep Fakes & the Law 🛡️
Enjoying the show? Support our mission and help keep the content coming by buying us a coffee.
Welcome to the Deep Dive. AI is no longer a futuristic concept—it's a critical part of our daily lives, from the devices in our pockets to the systems making decisions about our jobs and healthcare. But with this incredible speed of advancement comes an urgent wake-up call about how we ensure this technology serves us ethically and legally.
In this episode, we're taking a deep dive into the urgent issues of AI Ethics in 2025. We'll start by unpacking algorithmic bias, revealing how it's not just a technical glitch but a reflection and amplification of existing societal inequalities. You'll learn how bias can enter the AI pipeline at every stage—from flawed data to the design of the algorithm itself—and its profound, often life-altering, impacts on hiring (as seen in the infamous Amazon case study) and healthcare.
Next, we'll confront the new ethical and legal challenges of AI-generated content. We'll discuss the legal quagmire of copyright ownership, and the global threat of deep fakes that are eroding public trust and being used to damage reputations and influence elections. We'll also reveal how a new trend called "prompt injection" is challenging the integrity of scholarly publishing by attempting to game AI reviewers.
Finally, we'll explore the frameworks and strategies being developed to keep innovation responsible. You'll learn about landmark legislation like the EU AI Act, and the innovative state-level initiatives in California, Colorado, and New York City. We’ll outline a practical roadmap for building responsible AI, from ensuring diverse and representative data to employing human-in-the-loop systems and embracing radical transparency. We’ll also highlight how AI can be a force for good, actively identifying and reducing gender inequalities in pay and finance.
Tune in to understand these urgent issues and to ponder the question: how will we ensure that our collective commitment to integrity evolves fast enough to keep pace with the machines we create?
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By Tech’s Ripple Effect PodcastEnjoying the show? Support our mission and help keep the content coming by buying us a coffee.
Welcome to the Deep Dive. AI is no longer a futuristic concept—it's a critical part of our daily lives, from the devices in our pockets to the systems making decisions about our jobs and healthcare. But with this incredible speed of advancement comes an urgent wake-up call about how we ensure this technology serves us ethically and legally.
In this episode, we're taking a deep dive into the urgent issues of AI Ethics in 2025. We'll start by unpacking algorithmic bias, revealing how it's not just a technical glitch but a reflection and amplification of existing societal inequalities. You'll learn how bias can enter the AI pipeline at every stage—from flawed data to the design of the algorithm itself—and its profound, often life-altering, impacts on hiring (as seen in the infamous Amazon case study) and healthcare.
Next, we'll confront the new ethical and legal challenges of AI-generated content. We'll discuss the legal quagmire of copyright ownership, and the global threat of deep fakes that are eroding public trust and being used to damage reputations and influence elections. We'll also reveal how a new trend called "prompt injection" is challenging the integrity of scholarly publishing by attempting to game AI reviewers.
Finally, we'll explore the frameworks and strategies being developed to keep innovation responsible. You'll learn about landmark legislation like the EU AI Act, and the innovative state-level initiatives in California, Colorado, and New York City. We’ll outline a practical roadmap for building responsible AI, from ensuring diverse and representative data to employing human-in-the-loop systems and embracing radical transparency. We’ll also highlight how AI can be a force for good, actively identifying and reducing gender inequalities in pay and finance.
Tune in to understand these urgent issues and to ponder the question: how will we ensure that our collective commitment to integrity evolves fast enough to keep pace with the machines we create?