What is it about?
In today’s world, we’re drowning in information. This overload can push our brains to take shortcuts. "Statistical discrimination" is when we judge someone based on a preconceived notion of their group, simply because it's faster than getting to know them. Could this type of bias emerge on its own, without any of the complex social and historical baggage we see in humans? We use AI agents to answer this question. Our AI agents learned to take the "easy path." Instead of assessing partners based on their actions, they used group identity as a proxy for quality. The good news? We could de-bias them with a simple, unbiased tool—like an honest reputation system.
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Why is it important?
Discrimination is affecting our societies, and at its core, it is a failure to treat people as individuals. Discrimination doesn't have to be an inherent dislike for certain type of people, and can instead arise from cognitive short-cuts in a world of increasing (mis)information abundance. By understanding how discrimination emerges, and how we can ameliorate it, we can design our laws, norms and institutions to make sure we treat people according to who they are, not how they look.
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This page is a summary of: Perceptual interventions ameliorate statistical discrimination in learning agents, Proceedings of the National Academy of Sciences, June 2025, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2319933121.
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