Optimal Data-Driven Hiring With Equity for Underrepresented Groups
Abstrak
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.
Penulis (2)
Yinchu Zhu
Ilya O. Ryzhov
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Total Sitasi
- 3×
- Sumber Database
- CrossRef
- DOI
- 10.1177/10591478231224942
- Akses
- Open Access ✓