mlsanityData to 14 Oct 2026

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

arXiv 1712.00064 ↗First posted 30 Nov 2017cs.GT q-fin.GNcited by 44

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 44 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 11; 2022: 11; 2023: 14; 2024: 3; 2025: 3; 2026: 2.

44 papers.

Sort:

By area:

  1. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  2. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  3. 2023Performative Prediction: Past and Futurecited by 53
  4. 2021Stateful Strategic Regressioncited by 32
  5. 2022Fairness Transferability Subject to Bounded Distribution Shiftcited by 28
  6. 2023Long-Term Fairness with Unknown Dynamicscited by 27
  7. 2021Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systemscited by 21
  8. 2021Strategic Rankingcited by 21
  9. 2022Achieving Long-Term Fairness in Sequential Decision Makingcited by 19
  10. 2021Unintended Selection: Persistent Qualification Rate Disparities and Interventionscited by 18
  11. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  12. 2022Blackbox Post-Processing for Multiclass Fairnesscited by 14
32 more
  1. 2021Towards Return Parity in Markov Decision Processescited by 13
  2. 2023A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systemscited by 13
  3. 2022Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settingscited by 12
  4. 2023Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approachcited by 12
  5. 2021Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracingcited by 8
  6. 2022Enforcing Delayed-Impact Fairness Guaranteescited by 8
  7. 2023Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Makingcited by 8
  8. 2023Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factorscited by 7
  9. 2021Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instabilitycited by 5
  10. 2021Addressing the Long-term Impact of ML Decisions via Policy Regretcited by 5
  11. 2023Algorithmic Censoring in Dynamic Learning Systemscited by 5
  12. 2022Should attention be all we need? The epistemic and ethical implications of unification in machine learningcited by 4
  13. 2022Manifestations of Xenophobia in AI Systemscited by 4
  14. 2024Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learningcited by 4
  15. 2024Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challengescited by 4
  16. 2022Modelling the long-term fairness dynamics of data-driven targeted help on job seekerscited by 2
  17. 2022A Survey on Preserving Fairness Guarantees in Changing Environmentscited by 2
  18. 2023Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancementscited by 2
  19. 2023Insights From Insurance for Fair Machine Learningcited by 2
  20. 2023Fairness Vs. Personalization: Towards Equity in Epistemic Utilitycited by 2
  21. 2025A Causal Lens for Learning Long-term Fair Policiescited by 2
  22. 2024Understanding Decision Subjects' Engagement with and Perceived Fairness of AI Models When Opportunities of Qualification Improvement Existcited by 1
  23. 2025Privilege Scorescited by 1
  24. 2026Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspectivecited by 1
  25. 2021Toward a Fairness-Aware Scoring System for Algorithmic Decision-Makingcited by 0
  26. 2022Offsetting Unequal Competition through RL-assisted Incentive Schemescited by 0
  27. 2023Systemic Fairnesscited by 0
  28. 2023Strategic Evaluation: Subjects, Evaluators, and Societycited by 0
  29. 2023Interventions Against Machine-Assisted Statistical Discriminationcited by 0
  30. 2023Optimal Pricing for Data-Augmented AutoML Marketplacescited by 0
  31. 2025You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Timecited by 0
  32. 2026Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiringcited by 0