AI is leading the economic charge. In fact, without the massive investments in AI, our economy would look a lot worse right now. But what are the social and political costs that we incur? My guest, Karen Yeung, a professor at Birmingham Law School and School of Computer Science, argues that investments in AI our consolidating power while disempowering the rest of society. Our individual autonomy and our collective cohesion are simultaneously eroding. We need to push back - but how? And on what grounds? To what extent is the problem our socio-economic system or our culture or government (in)action? These questions and more in a particularly fun episode (for me, anyway).

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The engineering and data science students of today are tomorrow’s tech innovators. IF we want them to develop ethically sound technology, they better have a good grip on what ethics is all about. But how should we teach them? The same way we teach ethics in philosophy? Or is something different needed given the kinds of organizational forces they’ll find themselves subject to once they’re working. Steven Kelts, a lecturer in Princeton’s School of Public and International Affairs and in the Department of Computer Science researches this subject and teaches those very students himself. We explore what his research and his experience shows us about how we can best train our computer scientists to take the welfare of society into their minds and their work.

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Giving AI systems autonomy in a military context seems like a bad idea. Of course AI shouldn’t “decide” which targets should be killed and/or blown up. Except…maybe it’s not so obvious after all. That’s what my guest, Michael Horowitz, formerly of the DOD and now a professor at the University of Pennsylvania argues. Agree with him or not, he makes a compelling case we need to take seriously. In fact, you may even conclude with him that using autonomous AI in a military context can be morally superior to having a human pull the trigger.

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In August, I recorded a discussion with David Ryan Polgar, Founder of the nonprofit All Tech Is Human, in front of an audience of around 200 people. We talked about how AI mediated experiences make us feel sadder, that the tech companies don’t really care about this, and how people can organize to push those companies to take our long term well being more seriously.

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Wendell Wallach, who has been in the AI ethics game longer than just about anyone and has several books to his name on the subject, talks about his dissatisfaction with talk of “value alignment,” why traditional moral theories are not helpful for doing AI ethics, and how we can do better.

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It would be crazy to attribute legal personhood to AI, right? But then again, corporations are regarded as legal persons and there seems to be good reason for doing so. In fact, some rivers are classified as legal persons. My guest, David Gunkel, author of many books including “Person Thing Robot” argues that the classic legal distinction between ‘person’ and ‘thing’ doesn’t apply well to AI. How should we regard AI in a way that allows us to create it in a legally responsible way? All that and more in today’s episode

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LLMs behave in unpredictable ways. That’s a gift and a curse. It both allows for its “creativity” and makes it hard to control (a bit like a real artist, actually). In this episode, we focus on the cyber risks of AI with Walter Haydock, a former national security policy advisor and the Founder of StackAware.

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AI can stand between you and getting a job. That means for you to make money and support yourself and your family, you may have to convince an AI that you’re the right person for the job. And yet, AI can be biased and fail in all sorts of ways. This is a conversation with Hilke Schellmann, investigative journalist and author of ‘The algorithm” along with her colleague Mona Sloane, Ph.D., is an Assistant Professor of Data Science and Media Studies at the University of Virginia. We discuss Hilke’s book and all the ways things go sideways when people are looking for work in the AI era. Originally aired in season one.

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We want accurate AI, right? As long as it’s accurate, we’re all good? My guest, Will Landecker, CEO Accountable Algorithm, explains why accuracy is just one metric among many to aim for. In fact, we have to make tradeoffs across things like accuracy, relevance, and normative (including ethical) considerations in order to get a usable model. We also cover whether explainability is important and whether it’s even on the menu and the risks of multi-agentic AI systems.

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Should we allow autonomous AI systems? Who is accountable if things go sideways? And how is AI going to transform the future of military work? All this and more with my guest, Rosaria Taddeo, Professor of Digital Ethics and Defense Technologies at the University of Oxford.

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