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

By Lily Hu, Yiling Chen. 2017

144 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Management Science and Operations Research, AI Ethics, Fairness & Society, Economics & Operations Research

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Citations in the corpus

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “These techniques are already common in lending [52, 84, 42], hiring [88, 89, 55], and online advertising [119], and increasingly figure into decisions regarding pretrial detention [3, 80, 27], immigration detention [77], child maltreatment screening [122, 34, 13], public health [95, 105], and welfar”
Efficient candidate screening under multiple tests and implications for fairness (2019, background): “More recently, a number of papers have drawn inspiration from economic modeling, extending the literature on fairness in classification to consider longer-term dynamics, equilibria, and the emergence of feedback loops [10, 9, 7].”
The Frontiers of Fairness in Machine Learning (2018, background): “…markov decision process [JJK + 17], considering the equilibrium eﬀects of imposing statistical deﬁnitions of fairness in a model of a labor market [HC18], specifying the functional relationship between classiﬁcation outcomes and quality [LDR + 18], or by considering the eﬀect of a classiﬁer on a…”
Algorithmic Fairness (2020, background): “This field is sometimes referred to as fair pipelines [21, 45, 52, 69, 97].”
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach (2020, background): “Fairness in dynamical systems has been explored in speciﬁc domains: predictive policing (Lum & Isaac, 2016; Ensign et al., 2018), hiring (Hu & Chen, 2018; Hu et al., 2019), lending (Mouzannar et al., 2019), and RSs (Chaney et al., 2018; Bountouridis et al., 2019).”
Performative Prediction (2020, background): “See, for example, Liu et al. (2018), Hu & Chen (2018), and Hashimoto et al. (2018).”
Downstream Effects of Affirmative Action (2018, background): “Hu and Chen [9] study a dynamic model of the labor market similar to that of [5, 6] in which two populations are symmetric, but can choose to exert costly effort in order to improve their value to an employer.”
How Do Classifiers Induce Agents to Invest Effort Strategically? (2018, background): “Work exploring other aspects of how evaluation rules lead to investment of effort can be found in the economics literature, particularly in the contexts of hiring [16, 22] and affirmative action [8].”
From Fair Decision Making To Social Equality (2018, background): “This is compatible with the work on statistical discrimination [5, 12].”
Fair Decisions Despite Imperfect Predictions (2019, background): “Our work is most closely related to a recent line of work that analyzes the long-term eﬀects of consequential decisions informed by data-driven predictive models on underrepresented groups (Hu & Chen, 2018; Liu et al., 2018; Mouzannar et al., 2019; Tabibian et al., 2019).”
Optimal Decision Making Under Strategic Behavior (2019, methodology): “mentally different and their technical contributions are orthogonal to ours. More broadly, our work also relates to several recent studies on the long-term consequences of machine learning algorithms [10, 15, 17, 21] and recommender systems [19, 20]. Once we focus on the utility of a decision policy”
A Review on Fairness in Machine Learning (2022, background): “This field is sometimes referred to as fair pipelines [29, 61, 71, 99, 142].”
Algorithmic Fairness: Choices, Assumptions, and Definitions (2021, background): “For a benevolent social planner, this may be some notion of justice or social welfare (Hu & Chen 2018b).”
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning (2018, background): “In an employment scenario, Hu and Chen (2018) describe a dynamic, theoretical model where applying group-based thresholds incentivizes under-represented groups to invest in education and training, leading to a better long-term equilibrium in which group-based thresholds are no longer needed.”