Fairness at Equilibrium in the Labor Market

Abstract

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 Labor Market, 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. e 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.

Fairness Under Composition

“…Two subsequent works in this general vein explore approximating individual fairness with the help of an oracle that knows the task-specific metric [15,7]. Several works also consider how feedback loops can influence fair classification [10,20].…”

“…Here we must understand what is the salient outcome of the computation. For example, when reasoning about whether the college admissions system is fair, the salient outcome may be whether a student is accepted to at least one college, and not whether the student is accepted to a specific college 10 . Even if each college uses a fair classifier, the question is whether the "OR" of the colleges decisions is fair.…”

Mechanism design for social good

“…The aim is to choose hiring policies that will mitigate discrimination against protected categories. Hu and Chen [2017] highlights additional complexity that arises in dynamic settings where workers are hired based on investment decisions (e.g. college GPA) in an initial temporary labor market (e.g.…”

Bias Dilemma

“…Reliance on biased models can exacerbate marginalization of vulnerable groups. For instance, the ethical issues related to statistical models in hiring have been widely discussed (e.g., Hu and Chen 2017). Problems can arise even when models are deployed to reduce the influence of bias.…”