AITEC Philosophy Podcast

What if AI could improve democratic decision-making while simultaneously undermining democracy itself?In this episode of the AITEC Philosophy Podcast, Roberto and Sam speak with philosopher Lorenzo Manuali about his paper, When LLMs Threaten Democratic Autonomy. The conversation begins with a provocative thought experiment: imagine an AI that governs society better than humans ever could. Would something still be lost if we were no longer genuinely governing ourselves?Manuali argues that this problem is no longer merely hypothetical. Large language models are already being explored as tools for democratic participation. Facilitative LLMs can moderate deliberation, summarize discussions, and help groups reach decisions, while representative LLMs can attempt to predict what citizens would prefer—even about policies they have never considered.But these possibilities raise difficult questions. Could AI moderation flatten minority perspectives? Is an AI-generated prediction of what you would want equivalent to actually forming that political intention yourself? And when comparing AI with human moderators—who are themselves vulnerable to conformity, groupthink, and bias—what should the appropriate standard be?The conversation explores perspectival homogenization, persistent minorities, joint intention, collective self-determination, and the difference between better political outcomes and genuine democratic authorship. At stake is a fundamental question about democracy in the age of AI: Is it enough for our political institutions to make good decisions, or must those decisions genuinely be ours?For more info, visit ethicscircle.org. 

What if the biggest threat to privacy isn’t technology itself—but the laws that let powerful institutions use it without meaningful accountability?In this episode of the AITEC Philosophy Podcast, Roberto speaks with privacy scholar Daniel J. Solove about his book On Privacy and Technology. They explore why privacy is a social good rather than merely an individual preference, why the familiar “notice and consent” model places an impossible burden on ordinary people, and how AI makes modern data collection even harder to understand or control. The conversation also turns to algorithmic prediction, Wisconsin v. Loomis, the illusion of meaningful human oversight, and the ways biased data can shape decisions about sentencing, hiring, lending, and other consequential areas of life.Solove argues that technology itself is not the enemy. The deeper problem is a legal system that often fails to hold companies accountable for the risks they create. The solution, he suggests, is to change the incentives: make organizations bear more of the risk, require greater care before technologies are deployed, and impose meaningful consequences when things go wrong.Link to Daniel Solove's Substack: https://danielsolove.substack.com/Link to Daniel Solove's Website: https://www.danielsolove.com/

AI can speed up your work flow. But does it make you any smarter?In this episode of the AITEC Philosophy Podcast, Roberto and Sam talk with Jiayin Zhi, a PhD student in computer science at the University of Chicago, about what large language models are doing to our thinking. Her research asks a question that is becoming harder to avoid: when we use AI to interpret, write, and reason, are we extending our minds—or outsourcing them?The conversation begins with human-centered computing, the idea that technology should be designed around human needs, values, and real cognitive habits. From there, Jiayin shares findings from her research on AI-assisted close reading. One surprising result: AI-generated interpretations can improve the final written product, but too much AI can crowd out the pleasure of discovery and leave people feeling like there is little room for their own interpretation.The episode then turns to critical thinking. Here, timing matters. Using AI from the start—especially under time pressure—can produce polished work without deep understanding. But using AI later, after doing independent thinking first, may help reduce myside bias by introducing counterarguments and alternative perspectives.Along the way, we discuss poetry, interpretation, cognitive offloading, copy-and-paste learning, time pressure, myside bias, and the difference between using AI as a shortcut and using it as a genuine thinking partner.This episode is for anyone who wants to use AI well without surrendering the struggle that makes learning real.For more info, go to ethicscircle.org. 

When an AI tells a joke, writes an essay, or solves a complex programming problem, is it just performing a statistical magic trick—or is there something deeper happening under the hood?On this episode of The AITEC Philosophy Podcast, Roberto and Sam sit down with computer scientist John McCormick, author of Thinking AI: How Artificial Intelligence Emulates Human Understanding (Princeton University Press).For over 70 years, we've been asking Alan Turing's classic question: Can a machine think?. John reframes this debate by taking us past the polished, fluent surface of modern Large Language Models to look directly at the underlying code. We discuss whether AI merely mirrors human output or if its internal structures have begun to replicate pockets of human cognition. Despite the existential dread surrounding superintelligence, John shares a surprisingly optimistic perspective: even if we build entities that exceed human intellect, they will never replace the deeply rooted, biological fulfillment of our shared humanity.Uncover the machinery behind the curtain and join the conversation at ethicscircle.org.

Are we trading our critical thinking skills for the sake of digital convenience?In this episode of The AITEC Philosophy Podcast, Roberto Carlos García sits down with Michael Gerlich. Michael is the Head of the Center for Strategic Corporate Foresight and Sustainability, the Head of Executive Education, and a Senior Faculty member at SBS Swiss Business School. Most recently, Michael summarized his research on the interaction between LLMs and humans in The Convenience Trap: What Happens When AI Becomes the Mind Behind Our Lives. In this conversation, Michael shares his interdisciplinary research into how AI is "creeping" into nearly every aspect of our existence. We explore the dangerous phenomenon of "cognitive offloading"—the tendency to let algorithms make our choices, from the music we hear to the news we consume—and how this creates a "convenience trap" that narrows our perspective and weakens our mental "musculature". Michael argues that for AI to be a truly beneficial "sparring partner," we must do the hard work of thinking first before engaging with the machine.Join the conversation and learn how to keep the power of thought in your own hands at ethicscircle.org.Links: Michael’s book One of Michael’s articles on cognitive offloading Michael’s article on societal bifurcation

Have you ever suspected that the technology you use isn't just a tool, but an entirely fake reality replacing the natural world?On this episode of The AITEC Podcast, Sam Bennett sits down with Israeli philosopher Yochai Ataria to explore the brilliant philosophical undercurrents of Ridley Scott's 1982 classic, Blade Runner. Ataria reveals how the film functions as a profound Heideggerian critique of the modern technological age.They unpack how the film's protagonist, Rick Deckard, serves as a direct stand-in for René Descartes, undergoing a radical crisis of certainty and identity. The conversation also delves into ancient Greek frameworks, exploring how the replicant Roy Batty mirrors the Oedipus myth and how the rare flashes of lightning in a polluted sky tie back to Zeus. Ultimately, this episode asks whether the representations we rely on—from futuristic photo-analyzers to modern social media algorithms—are actually elaborate lies designed to disconnect us from reality.

Have you ever wondered if AI models actually understand the words they generate, or if they are just really good at faking it?On this episode of The AITEC Podcast, Roberto García and Sam Bennett are joined by philosopher Jacob Browning (Baruch College, CUNY) to unpack his article, Intentionality All-Stars Redux: Do language models know what they are talking about?Using a clever baseball diamond metaphor and drawing on the philosophy of Immanuel Kant, Jacob explains why Large Language Models lack the "intentionality" required for genuine comprehension. We cover: First Base (Formal Competence): Why LLMs struggle with basic logic and negation, revealing the absence of an underlying logical engine. Second Base (Rationality): Why true understanding requires purposive behavior, and how LLMs hilariously fail at "intuitive physics" (like trying to inflate a couch to get it onto a roof). Shortstop (Objectivity and World Models): Why genuine understanding requires grasping an objective, mind-independent world that determines whether sentences are true or false. This position explores how LLMs lack a coherent "world model," causing them to fail at tasks that require intuitive physics and planning for counterfactual situations (like predicting where a billiard ball will go or playing simple video games). Third Base (The Unified Self): Why making a claim requires a persistent self that takes responsibility for its beliefs—something a next-token predictor simply cannot do.Whether you're exploring the intersection of AI, technology, and ethics, or just trying to figure out if your chatbot actually knows what it's saying, this conversation will give you the philosophical toolkit to see through the illusion.

Philosopher Andrea Pinotti joins us to discuss At the Threshold of the Image: From Narcissus to Virtual Reality. What begins as a conversation about image theory quickly becomes a sweeping exploration of immersion, identity, and the strange pull of simulated worlds. Why do we long to enter the image? What do we gain—and lose—when the frame disappears? Pinotti guides us from Paleolithic caves to VR headsets, through myths of Narcissus and Pygmalion, to Black Mirror’s digital afterlives. Along the way, we consider how virtual environments blur fiction and reality, evoke religious promises, and reshape what it means to be human.If you've ever wondered why virtual reality feels so real—or so dangerous—this episode is for you.For more, visit ethicscircle.org. 

Have you ever felt like ChatGPT genuinely understands you? What if the reality is that it doesn't even have the foundational capacity to "speak" to you at all?On this episode of The AITEC Podcast, Roberto Carlos García and Sam Bennett sit down with philosopher Justin Tiehen (University of Puget Sound) to unpack his fascinating new paper, LLM's Lack a Theory of Mind and So Can't Perform Speech Acts--A Causal Argument.Justin takes us on a deep dive into the philosophy of mind to explain why current Large Language Models, despite their impressive output, are essentially just faking it. We explore why next-token predictors are completely missing the causal architecture required to have a "Theory of Mind," and why, without that, they are fundamentally incapable of making assertions, giving orders, or performing true speech acts.Key Takeaways from this Episode: The Ladder of Causation: Why AI is stuck observing statistical correlations and cannot grasp true causal interventions or counterfactuals (drawing on Judea Pearl’s work). The Speech Act Problem: Why performing a true "speech act" requires the deliberate intention to influence another person's mind. Cheating the Benchmarks: How LLMs "cheat" on psychological exams like the Sally-Anne false-belief test simply by memorizing statistical patterns in text. The Threat of AI Blackmail: What it would actually look like if an AI possessed a Theory of Mind and strategically tried to manipulate human behavior to achieve its goals.Whether you are deeply invested in the philosophy of language or just trying to figure out how much you should trust your favorite AI assistant, this conversation will completely reframe how you view generative AI.Learn more about our work and join the conversation at ethicscircle.org.

In this episode of the AITEC Podcast, we’re joined by philosopher Mathilda Marie Mulert, a doctoral researcher at the Oxford Internet Institute, to explore one of the most difficult questions in contemporary tech ethics: when, if ever, is it morally permissible to simulate sexual violence? Drawing on her recent work on simulation ethics, Mulert examines video games, virtual environments, sex robots, and consensual role-play to challenge the assumption that “it’s just pretend.” We discuss the Gamers’ Dilemma, the limits of consent, and why moral context—not just content—matters when evaluating simulated wrongdoing.This conversation is philosophical, careful, and candid. Listener discretion is advised.Links: Mathilda’s Oxford Internet Institute Webpage Mathilda’s recent articleFor more, visit ethicscircle.org.