Optimal Data-Driven Hiring With Equity for Underrepresented Groups
提出一种最优的公平招聘政策，该政策在功能上依赖受保护属性但统计上不依赖，能大幅提升弱势群体公平性且目标值损失极小。
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.