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Designing equitable algorithms
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AI
DOI：10.1038/s43588-023-00485-4.png)
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Abstract
En 中文
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. While the adherence to fairness constraints has become common practice in the design of algorithms across many contexts, a more holistic approach should be taken to avoid inflicting additional burdens on individuals in all groups, including those in marginalized communities.
Keywords:
MOTOR-VEHICLE SEARCHES
PRETRIAL DETENTION
RACIAL BIAS
RACE
HEALTH
FAIRNESS
CARE
DISPARITIES
GENDER
IMPACT
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