A Short-term Intervention for Long-term Fairness in the Labor Market

Abstract

The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisions are bound by an input-output notion of "individual fairness." Instead, we construct a dynamic reputational model of the labor market that illustrates the reinforcing nature of asymmetric outcomes resulting from groups' divergent accesses to resources and as a result, investment choices. To address these disparities, we adopt a dual labor market composed of a Temporary Labor Market (TLM), in which firms' hiring strategies are constrained to ensure statistical parity of workers granted entry into the pipeline, and a Permanent Labor Market (PLM), in which firms hire top performers as desired. Individual worker reputations produce externalities for their group; the corresponding feedback loop raises the collective reputation of the initially disadvantaged group via a TLM fairness intervention that need not be permanent. We show that such a restriction on hiring practices induces an equilibrium that, under particular market conditions, Pareto-dominates those arising from strategies that statistically discriminate or employ a "group-blind" criterion. The enduring nature of equilibria that are both inequitable and Pareto suboptimal suggests that fairness interventions beyond procedural checks of hiring decisions will be of critical importance in a world where machines play a greater role in the employment process.

arXiv comment10 pages

References in corpus (1)

• On the (im)possibility of fairness

Cited by in corpus (35)

• Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
• The Measure and Mismeasure of Fairness
• A Clarification of the Nuances in the Fairness Metrics Landscape
• Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
• On Consequentialism and Fairness
• A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems
• What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective
• Optimal Decision Making Under Strategic Behavior
• Pretrained AI Models: Performativity, Mobility, and Change
• Performative Prediction
• Decisions, Counterfactual Explanations and Strategic Behavior
• Allocating Opportunities in a Dynamic Model of Intergenerational Mobility
• Causal Modeling for Fairness in Dynamical Systems
• Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
• Fair Machine Learning Under Partial Compliance
• Fair Decisions Despite Imperfect Predictions
• From the Fair Distribution of Predictions to the Fair Distribution of Social Goods: Evaluating the Impact of Fair Machine Learning on Long-Term Unemployment
• Modelling the long-term fairness dynamics of data-driven targeted help on job seekers
• Should attention be all we need? The epistemic and ethical implications of unification in machine learning
• Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing
• Unintended Selection: Persistent Qualification Rate Disparities and Interventions
• Dynamic Modeling and Equilibria in Fair Decision Making
• Consequential Ranking Algorithms and Long-term Welfare
• Strategic Evaluation: Subjects, Evaluators, and Society
• Intersectional Affirmative Action Policies for Top-k Candidates Selection
• Bandit Learning with Delayed Impact of Actions
• Towards Return Parity in Markov Decision Processes
• Fair Data Representation for Machine Learning at the Pareto Frontier
• The Social Cost of Strategic Classification
• Best vs. All: Equity and Accuracy of Standardized Test Score Reporting
• From Fair Decision Making to Social Equality
• Beyond traditional assumptions in fair machine learning
• Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions
• Fairness for Whom? Critically reframing fairness with Nash Welfare Product
• Stateful Strategic Regression