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Noise

Kahneman, Sibony, and Sunstein's 2021 extension of the framework from bias to random variability — and the theoretical foundation for understanding one of AI's most underappreciated benefits.
Noise: A Flaw in Human Judgment, published in 2021, is the culmination of Kahneman's later work and a coauthored extension of the heuristics-and-biases framework to a different error category. Where the original program focused on bias — systematic departures from ideal judgment in predictable directions — the Noise project focuses on noise — random variability in judgment, such that different judges reach different conclusions from identical cases, or the same judge reaches different conclusions at different times. The book argues that noise is a massive, underappreciated source of error in professional judgment (medicine, law, hiring, forecasting) and that AI systems, by applying consistent algorithms to every case, can dramatically reduce it. But noise reduction comes at a cost: the variability that produces errors also produces innovation, creativity, and the occasional brilliant deviation. A world with less noise is more consistent and less exceptional.
Noise
Noise

In The You On AI Field Guide

The book's central empirical claim is that noise — random variability — is as

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