# Downstream Effects of Affirmative Action

arXiv: https://arxiv.org/abs/1808.09004 · HTML: https://mlsanity.com/paper/1808.09004/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2018.

## Built on by (most cited first, 30 of 34)

- [Evaluation Gaps in Machine Learning Practice](https://mlsanity.com/paper/2205.05256/) (2022)
- [Stateful Strategic Regression](https://mlsanity.com/paper/2106.03827/) (2021)
- [Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness](https://mlsanity.com/paper/2303.17555/) (2023)
- [Large Language Models as Recommender Systems: A Study of Popularity Bias](https://mlsanity.com/paper/2406.01285/) (2024)
- [Strategic Ranking](https://mlsanity.com/paper/2109.08240/) (2021)
- [Achieving Long-Term Fairness in Sequential Decision Making](https://mlsanity.com/paper/2204.01819/) (2022)
- [What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective](https://mlsanity.com/paper/2206.04101/) (2022)
- [Multiaccurate Proxies for Downstream Fairness](https://mlsanity.com/paper/2107.04423/) (2021)
- [Fundamental Bounds on Online Strategic Classification](https://mlsanity.com/paper/2302.12355/) (2023)
- [Survey on Fair Reinforcement Learning: Theory and Practice](https://mlsanity.com/paper/2205.10032/) (2022)
- [Towards Return Parity in Markov Decision Processes](https://mlsanity.com/paper/2111.10476/) (2021)
- [The Long Arc of Fairness: Formalisations and Ethical Discourse](https://mlsanity.com/paper/2203.06038/) (2022)
- [Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines](https://mlsanity.com/paper/2207.02912/) (2022)
- [Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing](https://mlsanity.com/paper/2104.01634/) (2021)
- [Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline](https://mlsanity.com/paper/2203.07513/) (2022)
- [Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors](https://mlsanity.com/paper/2301.08987/) (2023)
- [Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets](https://mlsanity.com/paper/2404.13240/) (2024)
- [Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness](https://mlsanity.com/paper/2405.20447/) (2024)
- [Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instability](https://mlsanity.com/paper/2104.12037/) (2021)
- [Addressing the Long-term Impact of ML Decisions via Policy Regret](https://mlsanity.com/paper/2106.01325/) (2021)
- [Disaggregated Interventions to Reduce Inequality](https://mlsanity.com/paper/2107.00593/) (2021)
- [Counterfactuals for the Future](https://mlsanity.com/paper/2212.03974/) (2022)
- [Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges](https://mlsanity.com/paper/2406.06736/) (2024)
- [Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All?](https://mlsanity.com/paper/2101.10905/) (2021)
- [Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features](https://mlsanity.com/paper/2202.11499/) (2022)
- [Modelling the long-term fairness dynamics of data-driven targeted help on job seekers](https://mlsanity.com/paper/2208.08881/) (2022)
- [Counting Hours, Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security](https://mlsanity.com/paper/2504.07719/) (2025)
- [Fair and Optimal Cohort Selection for Linear Utilities](https://mlsanity.com/paper/2102.07684/) (2021)
- [Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty](https://mlsanity.com/paper/2205.06483/) (2022)
- [FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems](https://mlsanity.com/paper/2307.05857/) (2023)

## Abstract (arXiv)

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.