Ethical Machines

We tend to assume AI can’t have free speech rights, since there’s no one there to speak. Jeffrey Howard, author of Setting Fire to Reason and a political philosopher at University College London, argues that government restrictions on AI outputs based on content or viewpoint are still a free speech violation, because the people who would read or watch that content are the ones wronged. We talk about why banning an AI-generated video wrongs its audience the way banning Das Kapital wrongs its readers rather than Marx, whether words with no speaker behind them count as speech at all, whether AI companies have free speech rights of their own, and why he thinks platforms should label AI content rather than ban it.

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How far does a data scientist’s responsibility reach? Does it stop at building an accurate model, or does it extend to what the model does once it’s in use? Ben Green, professor at the University of Michigan, former data scientist for the City of Boston, and author of Algorithmic Realism: Data Science Practices to Promote Social Justice, thinks it extends a long way: being a good data scientist means thinking about overall impact. I press him on whether being good at data science and being an ethically good data scientist are the same thing, whether data scientists are in a position to see how their work will play out, what to do when they disagree about what a good outcome is, and how much we should ask of individual data scientists versus their field and the systems they work in.

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What happens when students turn to LLMs to learn about history? My guest, Nuno Moniz, Associate Research Professor at the University of Notre Dame, argues this can ultimately lead to mass confusion, which in turn can lead to tragic conflicts. There are at least three sources of that confusion: AI hallucinations, misinformation spreading, and biased interpretations of history getting the upper hand. Exactly how bad this can get and what we’re supposed to do about it isn’t obvious, but Nuno has some suggestions.. Previously aired in season three.

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Today I’m talking with Elisha Wiesel, chair of the Elie Wiesel Foundation and former CIO of Goldman Sachs. We talk about why the foundation won’t use AI to recreate his father (Nobel Prizer winner and author of "Night," which recounts his experience in Auschwitz), why he refuses to say what his father would have thought about today’s politics, whether LLMs should show where their ideas come from and whether anyone would care if they did, what the Talmud can teach us about preserving minority opinions and saying “I don’t know,” and what financial models and LLMs get wrong in the same ways.

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I roll my eyes at talk of "AI welfare," the idea that we should take seriously the notion that AI has interests or a well-being that we should take seriously. My guests, Geoff Keeling and Winnie Street, both Fellows at the Institute of Philosophy at University of London and co-authors of “Emerging Questions in AI Welfare," don't think that anything like today's AI models are conscious or have a welfare we need to concern ourselves with. But they do want to take seriously questions about whether they could ever have a welfare, what that would take, how morally costly it would be if we failed to attribute well-being to entities that have one and how costly it would be if we attribute well-being to entities that have none. Buckle up - it's a complex and fascinating discussion.

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