Optimal Data-Driven Hiring With Equity for Underrepresented Groups

• 成果类型: Article; Early Access
• 署名作者: Zhu, Yinchu; Ryzhov, Ilya O.
• 署名单位: Brandeis University; Brandeis University; University System of Maryland; University of Maryland College Park
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478231224942
• 发表日期: 2024
• 关键词: Data-driven decision-making; Prescriptive Analytics; equity in hiring; fair machine learning

摘要

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.