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Downstream Effects of Affirmative Action

Published: 29 January 2019 Publication History

Abstract

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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cover image ACM Conferences
FAT* '19: Proceedings of the Conference on Fairness, Accountability, and Transparency
January 2019
388 pages
ISBN:9781450361255
DOI:10.1145/3287560
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 January 2019

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Author Tags

  1. Long-term fairness
  2. affirmative action
  3. college admissions
  4. job market

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  • (2024)Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group FairnessProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658929(616-630)Online publication date: 3-Jun-2024
  • (2024)FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System2024 ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)10.1109/ICCPS61052.2024.00025(202-211)Online publication date: 13-May-2024
  • (2024)FAIRO: Fairness-aware Sequential Decision Making for Human-in-the-Loop CPS2024 ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)10.1109/ICCPS61052.2024.00015(87-98)Online publication date: 13-May-2024
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