Ethical Machines

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

Geoff Keeling and Winnie Street, philosophers at the University of London, study AI welfare and moral status. They explore whether future AI could have interests, consciousness, or subjective experience, and how we might investigate these possibilities. The conversation weighs evidence from behavior, architecture, and interpretability, while confronting the dangers of both neglecting a genuinely sentient system and granting moral standing to one that has none.

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

Christian B. Miller, a Wake Forest philosophy professor and author of The Honesty Crisis, explores how technology reshapes dishonest behavior. They discuss AI-written student papers, romantic AI companions and cheating, influencer culture’s ties to bullshit and self-deception, and pastors using AI-generated sermons. A lively tour of truth, trust, plagiarism, and digital temptation.

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

Jonathan Schaeffer thinks we're building AI the wrong way.While large language models have produced remarkable results, he argues that hallucinations, bias, and unreliability aren't bugs that can be fixed—they're consequences of the underlying architecture itself. In his view, LLMs are an important stepping stone, but not the path to the kind of AI we can truly trust.We discuss whether current AI systems are "good enough," automation bias, AI regulation, data centers, environmental costs, and the race toward AGI. We also debate whether society should slow down long enough to put meaningful guardrails in place before deploying increasingly powerful AI systems at scale.Jonathan Schaeffer is a Professor of Computing Science at the University of Alberta and a pioneer in artificial intelligence research.Advertising Inquiries: https://redcircle.com/brands

Mona Sloane, assistant professor and author of Predicted, studies how predictive AI reshapes social life. She explains how AI has become woven into digital systems that mediate relationships and behavior. They discuss how prediction scales into new domains, risks eroding deliberation and surprise, and possible public-utility and regulatory responses.

Science depends on more than just results. It depends on researchers asking questions, testing hypotheses, challenging assumptions, and scrutinizing evidence.My guest, Emily Sullivan, Senior Lecturer in Philosophy of Science and AI at the University of Edinburgh, argues that AI is beginning to influence every stage of the scientific process—from deciding which questions get asked to how papers are written, reviewed, and published.We discuss algorithmic monocultures, scientific de-skilling, AI-generated research, and whether the pressure to accelerate discovery risks undermining the very process that makes science reliable in the first place.I'm sympathetic to the promise of AI in science. Emily is concerned that, if we're not careful, we may end up optimizing for scientific output at the expense of scientific inquiry itself.Advertising Inquiries: https://redcircle.com/brands

Fabio Tollen, a postdoctoral researcher at the University of Edinburgh studying moral responsibility and AI, tackles who should be held to account for autonomous systems. He explores responsibility gaps and the many-hands problem. He distinguishes blameworthy moral responsibility from a thinner notion of moral answerability. He considers whether developers resemble parents or engineers and whether organizations can bear responsibility.