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

Hu, Lanlin; Chen, Yi‐Ling

doi:10.1145/3178876.3186044

Cited by 128 publications

(101 citation statements)

References 15 publications

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“…The assumption is that this binary classification is sufficient to characterize the utility that the individual would bring. This is compatible with the work on statistical discrimination [CL93,HC18]. An alternative way to reach this model is to first consider a more general multiclass characterization and then show that a threshold policy is optimal with respect to utility and societal improvement, as was done in [LDR + 18].…”

Section: Related Worksupporting

confidence: 61%

From Fair Decision Making To Social Equality

Mouzannar

Ohannessian

Srebro

2019

Proceedings of the Conference on Fairness, Accountability, and Transparency

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The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) have often the implicit goal of achieving balance among groups within the population. The most basic notion of balance is eventual equality between the qualifications of the groups. How can we incorporate influence dynamics in decision making? How well do dynamics-oblivious fairness policies fare in terms of reaching equality? In this paper, we propose a simple yet revealing model that encompasses (1) a selection process where an institution chooses from multiple groups according to their qualifications so as to maximize an institutional utility and (2) dynamics that govern the evolution of the groups' qualifications according to the imposed policies. We focus on demographic parity as the formalism of affirmative action.We then give conditions under which an unconstrained policy reaches equality on its own. In this case, surprisingly, imposing demographic parity may break equality. When it doesn't, one would expect the additional constraint to reduce utility, however, we show that utility may in fact increase. In more realistic scenarios, unconstrained policies do not lead to equality. In such cases, we show that although imposing demographic parity may remedy it, there is a danger that groups settle at a worse set of qualifications. As a silver lining, we also identify when the constraint not only leads to equality, but also improves all groups. This gives quantifiable insight into both sides of the mismatch hypothesis. These cases and trade-offs are instrumental in determining when and how imposing demographic parity can be beneficial in selection processes, both for the institution and for society on the long run. *

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Section: Related Worksupporting

confidence: 61%

From Fair Decision Making To Social Equality

Mouzannar

Ohannessian

Srebro

2019

Proceedings of the Conference on Fairness, Accountability, and Transparency

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“…Recent work by Hu and Chen [2018] considers a model for long-term outcomes and fairness in the labor market. They propose imposing the demographic parity constraint in a temporary labor market in order to provably achieve an equitable long-term equilibrium in the permanent labor market, reminiscent of economic arguments for affirmative action [Foster and Vohra, 1992].…”

Section: Related Workmentioning

confidence: 99%

Delayed Impact of Fair Machine Learning

Liu

Dean

Rolf

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

177

196

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Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect.We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably.Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.

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“…Jabbari et al study the costs (measured as their effect on the rate of learning) of imposing fairness constraints on learners in general Markov decision processes [10]. 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. They study a two stage model of a labor market in which interventions in a "temporary" labor market can lead to high welfare symmetric equilibrium in the long run.…”

Section: Related Workmentioning

confidence: 99%

Downstream Effects of Affirmative Action

Kannan

Roth

Ziani

2019

Proceedings of the Conference on Fairness, Accountability, and Transparency

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We study a two-stage model, in which students are 1) admitted to college on the basis of an entrance exam which is a noisy signal about their qualifications (type), and then 2) those students who were admitted to college can be hired by an employer as a function of their college grades, which are an independently drawn noisy signal of their type. Students are drawn from one of two populations, which might have different type distributions. We assume that the employer at the end of the pipeline is rational, in the sense that it computes a posterior distribution on student type conditional on all information that it has available (college admissions, grades, and group membership), and makes a decision based on posterior expectation. We then study what kinds of fairness goals can be achieved by the college by setting its admissions rule and grading policy. For example, the college might have the goal of guaranteeing equal opportunity across populations: that the probability of passing through the pipeline and being hired by the employer should be independent of group membership, conditioned on type. Alternately, the college might have the goal of incentivizing the employer to have a group blind hiring rule. We show that both goals can be achieved when the college does not report grades. On the other hand, we show that under reasonable conditions, these goals are impossible to achieve even in isolation when the college uses an (even minimally) informative grading policy.

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

Cited by 128 publications

References 15 publications

From Fair Decision Making To Social Equality

From Fair Decision Making To Social Equality

Delayed Impact of Fair Machine Learning

Downstream Effects of Affirmative Action

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