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Optimal Data-Driven Hiring With Equity for Underrepresented Groups
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AI
DOI：10.1177/10591478231224942.png)
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Abstract
En 中文
<jats:p> We present a data-driven prescriptive framework for fair decisions, motivated by hiring. An employer evaluates a set of applicants based on their observable attributes. The goal is to hire the best candidates while avoiding bias with regard to a certain protected attribute. Simply ignoring the protected attribute will not eliminate bias due to correlations in the data. We present a provably optimal fair hiring policy that depends on the protected attribute functionally, but not statistically. The policy does not set rigid quotas, and does not withhold information from decision-makers. Both synthetic and real data indicate that the policy can greatly improve equity for underrepresented and historically marginalized groups, often with negligible loss in objective value. </jats:p>
Keywords:
fair decision-making
prescriptive framework
hiring bias
protected attributes
data-driven policy
Journal
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7.4
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2.5K
Citations:
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