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Lessons for artificial intelligence from the study of natural stupidity


Artificial intelligence and machine learning systems are increasingly replacing human decision makers in commercial, healthcare, educational and government contexts. But rather than eliminate human errors and biases, these algorithms have in some cases been found to reproduce or amplify them. We argue that to better understand how and why these biases develop, and when they can be prevented, machine learning researchers should look to the decades-long literature on biases in human learning and decision-making. We examine three broad causes of bias—small and incomplete datasets, learning from the results of your decisions, and biased inference and evaluation processes. For each, findings from the psychology literature are introduced along with connections to the machine learning literature. We argue that rather than viewing machine systems as being universal improvements over human decision makers, policymakers and the public should acknowledge that these system share many of the same limitations that frequently inhibit human judgement, for many of the same reasons.

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Fig. 1: In illusory correlations, an agent mistakenly comes to believe that there is a correlation between a variable of interest and membership to a larger group (or more data-rich group or individual).
Fig. 2: An agent’s beliefs about whether an option is mostly good or mostly bad evolve as the agent experiences a series of positive and negative outcomes, potentially causing the hot stove effect.
Fig. 3: An ‘attentional learning trap’ can emerge with choice-contingent feedback in some environments.
Fig. 4: Reference-dependent risk preferences can be produced by Bayesian prediction.


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Correspondence to Alexander S. Rich.

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A.S.R. is employed by Flatiron Health, an independent subsidiary of Roche.

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Rich, A.S., Gureckis, T.M. Lessons for artificial intelligence from the study of natural stupidity. Nat Mach Intell 1, 174–180 (2019).

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