Optimal data-driven hiring with equity for underrepresented groups

TL;DR

This paper introduces a data-driven, fair hiring framework that optimally balances candidate quality with equity for underrepresented groups, addressing bias in decision-making processes.

Contribution

It proposes a novel fair hiring policy that depends on protected attributes functionally rather than statistically, ensuring optimality and fairness.

Findings

The policy effectively reduces bias in synthetic and real datasets.

It improves equity for underrepresented and marginalized groups.

The approach demonstrates practical potential for fair hiring applications.

Abstract

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 hiring policy that depends on the protected attribute functionally, but not statistically, and we prove that, among all possible fair policies, ours is optimal with respect to the firm's objective. We test our approach on both synthetic and real data, and find that it shows great practical potential to improve equity for underrepresented and historically marginalized groups.