Designing equitable algorithms

Predictive algorithms are now commonly used to distribute society's resources and sanctions. But these algorithms can entrench and exacerbate inequities. To guard against this possibility, many have suggested that algorithms be subject to formal fairness constraints. Here we argue, however, tha...

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Vydané v:Nature Computational Science Ročník 3; číslo 7; s. 601 - 610
Hlavní autori: Chohlas-Wood, Alex, Coots, Madison, Goel, Sharad, Nyarko, Julian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Nature Publishing Group 01.07.2023
Predmet:
Algorithms
Body mass index
Constraints
Decision making
Diabetes
Ethnicity
Hispanic Americans
Patients
Principles
Social exclusion
United States > US
ISSN:2662-8457, 2662-8457
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Shrnutí:Predictive algorithms are now commonly used to distribute society's resources and sanctions. But these algorithms can entrench and exacerbate inequities. To guard against this possibility, many have suggested that algorithms be subject to formal fairness constraints. Here we argue, however, that popular constraints-while intuitively appealing-often worsen outcomes for individuals in marginalized groups, and can even leave all groups worse off. We outline a more holistic path forward for improving the equity of algorithmically guided decisions.
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ISSN:2662-8457
2662-8457
DOI:10.1038/s43588-023-00485-4