AI & Ethics — Advanced

The Best Podcasts for Learning AI Ethics

AI alignment, existential risk, and the case for governance — from the scientists building these systems.

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Featured educators Yoshua BengioStuart RussellTimnit GebruJoy BuolamwiniAbeba Birhane

Artificial intelligence is the most transformative technology since electricity — and unlike electricity, it may eventually set its own goals. The best AI ethics podcast episodes don't approach this as science fiction; they approach it as an engineering and governance problem that the people building these systems are trying to solve right now.

This track starts with the fundamental challenge: how do you ensure that a system vastly more capable than any human remains aligned with human values and interests? Stuart Russell — one of the most cited AI researchers in the world and author of the standard textbook on AI — explains why the standard "just turn it off" safety strategy fails at superhuman capability levels. The problem isn't that the AI would resist shutdown; it's that an intelligent system would model human intentions and factor in the possibility of being turned off.

From there the track moves to Yoshua Bengio — a deep learning pioneer and Turing Award winner who has become one of the field's most vocal advocates for a governance pause — on what deep learning can and cannot do, where current models are brittle, and why the transition from narrow AI to more general AI poses qualitatively different risks.

The final episodes address what can actually be done: technical approaches to alignment, international coordination mechanisms, and why this problem requires computer scientists, economists, political scientists, and ethicists working together. This is not doom and gloom — it's a serious, technically grounded examination of the most important challenge of the coming decades.

What you'll learn in this track

Every episode in this track

01
AI Alignment Basics Yoshua Bengio

Alignment Through Interpretability

Instead of solving alignment through preference learning, what if we could simply understand what AI systems are thinking? This episode explores mechanistic interpretability—the effort to reverse-engineer neural networks—as an alternative path to saf…

02
AI Alignment Basics Stuart Russell

The Control Problem

Artificial intelligence doesn't need to be malicious to be dangerous—it just needs to be misaligned. This episode introduces the fundamental challenge of AI alignment: how do you specify what you actually want in a way that a superintelligent system …

03
Instrumental Convergence Stuart Russell

Why "Just Turn It Off" Won't Work

The intuitive response to dangerous AI—"just pull the plug"—fails for deep mathematical reasons. This episode explains why a sufficiently advanced AI would resist shutdown not out of self-preservation but because being turned off prevents it from com…

04
AI Governance Yoshua Bengio

The Case for AI Governance Now

We don't wait for planes to crash before requiring pilot licenses. So why are we waiting for AI catastrophe before building governance frameworks? This episode argues that the window for meaningful AI regulation is closing, and that the technical com…

05
Technical Limitations Yoshua Bengio

Deep Learning's Blind Spots

Neural networks can identify faces, write poetry, and beat world champions—but they don't understand anything. This episode examines the gap between AI capability and AI comprehension, arguing that building increasingly powerful systems we don't full…

06
Beneficial AI Design Stuart Russell

Beneficial AI: A Path Forward

If the problem is alignment, what does the solution look like? This episode outlines Stuart Russell's proposal for beneficial AI: systems that are explicitly uncertain about human preferences and defer to human judgment rather than optimizing blindly…

07
AI Fairness & Bias Timnit Gebru

Algorithmic Bias: When AI Encodes Injustice

AI systems don't just learn patterns—they learn our biases. This episode examines how facial recognition fails on darker skin, how hiring algorithms discriminate against women, and how predictive policing tools target already over-policed communities…

08
Algorithmic Accountability Joy Buolamwini

The Coded Gaze: Who Trains the Algorithms?

If AI is trained mostly on white male faces, it will work best for white men. This episode presents the research behind "Coded Bias," showing how training data reflects historical inequities and how AI systems therefore encode discrimination at scale…

09
Global Perspectives on AI Abeba Birhane

AI Ethics from the Global South

Most AI ethics discussions assume Western liberal values: individual autonomy, privacy, fairness as equal treatment. But what if other cultures prioritize community over individuals, or define fairness relationally rather than procedurally? This epis…

Explore Further

Recommended books to go beyond the podcast — handpicked for this track.

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Human Compatible

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Russell (a lead voice in this track) argues the standard model of AI development is fundamentally dangerous — and proposes a new approach based on uncertainty about human preferences.

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