Is AI Risk Really New?

We tend to talk about AI risk as something brand new. Shana Morgan, Global Head of AI/Privacy at L3Harris Technologies, says the risks are old ones we never addressed, like bias, privacy, security and mental health, and AI amplifies them at scale. Treating them as new, she argues, gives organizations an out on accountability. We talk about why policy shouldn’t lead AI governance, why privacy, legal, IP and security teams need to stop working in silos, how working on AI differs from working on privacy, and why employees need basic AI education explained simply enough for your grandmother.Advertising Inquiries: https://redcircle.com/brands

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.Advertising Inquiries: https://redcircle.com/brands

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.Advertising Inquiries: https://redcircle.com/brands

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. Advertising Inquiries: https://redcircle.com/brands

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.Advertising Inquiries: https://redcircle.com/brands

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.Advertising Inquiries: https://redcircle.com/brands

We often think of AI companions as bad, or at least suspect. Giada Pistilli, formerly Principal Ethicist at Hugging Face and now at Mistral, wants to test that assumption, and she's done a lot of research to back up her view. In her new book, The Essential Guide to AI Companions, she looks at people who form emotional relationships with chatbots as partners, as therapists, and as stand-ins for parents who have died. We talk about how people end up in these relationships, why most of them weren't looking for one, whether attachment to a chatbot is a problem in itself, and whether chatbot memory should be off by default.Advertising Inquiries: https://redcircle.com/brands

My guest today is Christian B. Miller, professor of philosophy and author of The Honesty Crisis. Christian's central claim is that technology (AI, social media, etc.) has created conditions where dishonesty is increasingly incentivized and easier to get away with. When you put those two together, you get an honesty crisis.We discuss, as some examples of growing dishonesty, students using LLMs to write papers and passing off the work as their own, romantic relationships with AI and whether having one could constitute cheating, celebrity and influencer culture's relationship to bullshit and self-deception, and the surprising problem of pastors plagiarizing sermons with AI. While the conversation was about an alleged and plausible crisis, it turned out to be a very fun one.Advertising Inquiries: https://redcircle.com/brands

Season four kicks off with Paula Goldman, author of the new book, “Manage the Machine: How to Harness Human AI Collaboration at Work,” Paula is also Salesforce’s Chief Ethical and Humane Use Officer. We talk about the expertise pipeline problem, why humans should be "at the help" instead of "in the loop," and the ways responsibility gets murky when dealing with teams comprised of people and AI agents.Advertising Inquiries: https://redcircle.com/brands

Here are three paradoxes, according to Virginia Dignum, my guest today: 1. The more capable AI becomes, the more it reveals the richness and complexity of human intelligence. 2. Less bias in AI does not necessarily create more justice. 3. The pursuit of artificial superintelligence may ultimately reveal that humanity's greatest intelligence is collective, not artificial.We discuss the limits of computation, the dangers of confusing data with reality, why AI ethics often misses deeper social problems, and what it would mean to build technology that genuinely serves human flourishing rather than replacing it. Our conversation is grounded in the book “The AI Paradox”, authored by Virginia, who is a professor in Responsible Artificial Intelligence and the Director of the AI Policy Lab at Umeå University in Sweden.Advertising Inquiries: https://redcircle.com/brands