Ethical Machines

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.

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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.

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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.

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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.

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My guest, Mona Sloane, author of Predicted: How AI Is Restructuring Social Life, argues that AI has become part of our social infrastructure. Its predictive systems increasingly shape how we work, find information, build relationships, and navigate society.
Mona worries that as prediction becomes embedded in more areas of life, we risk becoming less willing to deliberate, challenge assumptions, and shape our own futures. I push back on whether AI really should be understood as infrastructure and whether predictions made by AI are fundamentally different from the predictions humans have always made.
We also discuss democracy, power, regulation, and what happens when prediction becomes the dominant way of understanding the world.
Book: https://a.co/d/04GwwuFR

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