Statistically inaccurate and morally unfair judgements via base rate intrusion

Jack Cao (), Max Kleiman-Weiner and Mahzarin R. Banaji
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Jack Cao: Harvard University
Max Kleiman-Weiner: Massachusetts Institute of Technology
Mahzarin R. Banaji: Harvard University

Nature Human Behaviour, 2017, vol. 1, issue 10, 738-742

Abstract: Abstract From a statistical standpoint, judgements about an individual are more accurate if base rates about the individual’s social group are taken into account 1–4 . But from a moral standpoint, using these base rates is considered unfair and can even be illegal 5–9 . Thus, the imperative to be statistically accurate is directly at odds with the imperative to be morally fair. This conflict was resolved by creating tasks in which Bayesian rationality and moral fairness were aligned, thereby allowing social judgements to be both accurate and fair. Despite this alignment, we show that social judgements were inaccurate and unfair. Instead of appropriately setting aside social group differences, participants erroneously relied on them when making judgements about specific individuals. This bias—which we call base rate intrusion—was robust, generalized across various social groups (gender, race, nationality and age), and differed from analogous non-social judgements. Results also demonstrate how social judgements can be corrected to achieve both statistical accuracy and moral fairness. Overall, these data (total N = 5,138) highlight the pernicious effects of social base rates: under conditions that closely approximate those of everyday life 10–12 , these base rates can undermine the rationality and fairness of human judgements.

Date: 2017
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DOI: 10.1038/s41562-017-0218-y

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