Lily Hu and Yiling Chen
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 definition, 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 Labor Market, in which individual worker fairness is guaranteed. We show that such restrictions on hiring practices induces an equilibrium that Pareto dominates 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-fulfilling 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. e enduring nature of such equilibria that are both inequitable and Pareto inefficient suggest that fairness interventions are of critical importance in moving the labor market to be more socially just and efficient.