Ethical Machines

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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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.

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My guest, Fabio Tollon, a postdoctoral researcher on the BRAID programme at the University of Edinburgh, argues that answering that question is more difficult than it first appears. Traditional theories of moral responsibility suggest that people should only be blamed for actions they understand and control. But AI systems seem to challenge both requirements.
We discuss responsibility gaps, the problem of many hands, whether AI developers are more like parents or engineers, and Fabio's distinction between moral responsibility and moral answerability. Along the way, we explore whether answerability can help us make sense of AI harms when blame is difficult to assign.

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