Downstream Effects of Affirmative Action

By Sampath Kannan, Aaron Roth, Juba Ziani. 2018

92 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Sociology and Political Science

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Citations in the corpus

Beyond Individual and Group Fairness (2020, background): “Recent works have also studied the long-term impact of optimizing fairness criteria in settings with feedback mechanisms [Liu et al., 2018, Hashimoto et al., 2018, Mouzannar et al., 2019, Kannan et al., 2019].”
Making Decisions that Reduce Discriminatory Impacts (2019, unknown): “The first, by Kannan et al. (2018), formulates a two-stage model where (a) applicants are admitted to college by an exam and (b) college students can be hired by an employer based on their exam, grades, and protected attributes (i.e., race, sex).”
Evaluation Gaps in Machine Learning Practice (2022, background): “A simple model of the ecosystem’s data distributions is particularly risky when system feedback effects would cause the distributions of data in the ecosystem to diverge from those in the evaluation sample [92, 106].”
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach (2020, background): “In general, this work has focused on the role of algorithms in shaping environments over time (Hashimoto et al., 2018; Kannan et al., 2019), observing that the repeated application of algorithms in a changing environment impacts fairness in the long-term differently from short-term effects.”
On the Legal Compatibility of Fairness Definitions (2019, background): “, the demographics of the set of individuals receiving any classification are the same as the demographics of the underlying population), while treating similar individuals as similarly as possible” [17] and understands affirmative action to be cases in which we “explicitly take demographics into ac”