Fairness at Equilibrium in the Labor Market

Recent literature on computational notions of fairness has been broadly divided into two distinct camps, supporting interventions that address either individual-based or group-based fairness. Rather than privilege a single de€nition, we seek to resolve both within the particular domain of employment discrimination. To this end, we construct a dual labor market model composed of a Temporary Labor Market, in which €rm strategies are constrained to ensure group-level fairness, and a Permanent LaborMarket, in which individual worker fairness is guaranteed. We show that such restrictions on hiring practices induces an equilibrium that Paretodominates those arising from strategies that employ statistical discrimination or a “group-blind” criterion. Individual worker reputations produce externalities for collective reputation, generating a feedback loop termed a “self-ful€lling prophecy.” Our model produces its own feedback loop, raising the collective reputation of an initially disadvantaged group via a fairness intervention that need not be permanent. Moreover, we show that, contrary to popular assumption, the asymmetric equilibria resulting from hiring practices that disregard group-fairness may be immovable without targeted intervention. Œe enduring nature of such equilibria that are both inequitable and Pareto inecient suggest that fairness interventions are of critical importance in moving the labor market to be more socially just and ecient. ACM Reference format: Lily Hu and Yiling Chen. 2017. Fairness at Equilibrium in the Labor Market. In Proceedings of Fairness, Accountability, and Transparency in Machine Learning, Halifax, Nova Scotia, Canada, August 14–17 (FAT/ML ’17), 5 pages. DOI: 10.1145/nnnnnnn.nnnnnnn