Ethical Machines

AI is deployed across the globe. But how sensitive is it to the cultural contexts - ethics, norms, laws and regulations - in which it finds itself. My guest today, Rocky Clancy of Virginia Tech, argues that AI is too Western-focused. We need to engage in empirical research so that AI is developed in a way that comports with the people it interacts with, wherever they are.

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When we’re playing a game or a sport, we like being measured. We want a high score, we want to beat the game. Measurement makes it fun. But in work, being measured, hitting our numbers, can make us miserable. Why does measuring ourselves sometimes enhance and sometimes undermine our happiness and sense of fulfillment? That’s the question C. Thi Nguyen tackles in his new book “The Score: How to Stop Playing Somebody Else’s Game.” Thi is one of the most interesting philosophers I know - enjoy!

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When it comes to the foundation models that are created by the likes of Google, Anthropic, and OpenAI, we need to treat them as utility providers. So argues my guest, Joanna Bryson, Professor of Ethics and Technology at the Hertie School of Business in Berlin, Germany. She further argues that the only way we can move forward safely is to create a transnational coalition of the willing that creates and enforces ethical and safety standards for AI. Why such a coalition is necessary, who might be part of it, how plausible it is that we can create such a thing, and more are covered in our conversation.

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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.

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When thinking about AI replacing people, we usually look to the extremes: utopia and dystopia. My guest today, Finn Morehouse, a research fellow at Forethought, a nonprofit research organization, thinks that neither of these extremes are the most likely. In fact, he thinks that one reason that AI defies prediction is that it’s not a normal technology. What’s not normal about it? It’s not merely in the business of multiplying productivity, he says, but of replacing the standard bottleneck to greater productivity: humans.

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