Equal Merit Does Not Imply Equality: Discrimination at Equilibrium in a Hiring Market with Symmetric Agents

TL;DR

This paper explores how discrimination in hiring markets can arise from relational inequality and strategic bargaining, not just from unequal resource distribution, highlighting the importance of endogenous factors in ML fairness.

Contribution

It introduces a simple sequential model demonstrating non-distributional sources of inequality and analyzes how agents learn such strategies through online learning algorithms.

Findings

Asymmetric wages result from differences in bargaining strategies.

Equilibrium analysis shows non-distributional inequality mechanisms.

Online learning converges to asymmetric equilibria.

Abstract

Machine learning has grown in popularity to help assign resources and make decisions about users, which can result in discrimination. This includes hiring markets, where employers have increasingly been interested in using automated tools to help hire candidates. In response, there has been significant effort to understand and mitigate the sources of discrimination in these tools. However, previous work has largely assumed that discrimination, in any area of ML, is the result of some initial \textit{unequal distribution of resources} across groups: One group is on average less qualified, there is less training data for one group, or the classifier is less accurate on one group, etc. However, recent work have suggested that there are other sources of discrimination, such as relational inequality, that are notably non-distributional. First, we show consensus in strategy choice is a non-distributional source of inequality at equilibrium in games: We provide subgame perfect equilibria in a simple sequential model of a hiring market with Rubinstein-style bargaining between firms and candidates that exhibits asymmetric wages resulting from differences in agents' threat strategies during bargaining. Second, we give an initial analysis of how agents could learn such strategies via convergence of an online learning algorithm to asymmetric equilibria. Ultimately, this work motivates the further study of endogenous, possibly non-distributional, mechanisms of inequality in ML.