  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 firm 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. The 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.
