# Centralized Selection with Preferences in the Presence of Biases

arXiv: https://arxiv.org/abs/2409.04897 · HTML: https://mlsanity.com/paper/2409.04897/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 84 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees](https://mlsanity.com/paper/1806.06055/) (2018)
- [Ranking with Fairness Constraints](https://mlsanity.com/paper/1704.06840/) (2017)
- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?](https://mlsanity.com/paper/1912.01094/) (2019)
- [Bandit Learning in Decentralized Matching Markets](https://mlsanity.com/paper/2012.07348/) (2020)
- [Selection Problems in the Presence of Implicit Bias](https://mlsanity.com/paper/1801.03533/) (2018)
- [Interventions for Ranking in the Presence of Implicit Bias](https://mlsanity.com/paper/2001.08767/) (2020)
- [Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets](https://mlsanity.com/paper/2203.03684/) (2022)
- [On Fair Selection in the Presence of Implicit Variance](https://mlsanity.com/paper/2006.13699/) (2020)
- [Allocating Opportunities in a Dynamic Model of Intergenerational Mobility](https://mlsanity.com/paper/2101.08451/) (2021)
- [Towards Fair Recommendation in Two-Sided Platforms](https://mlsanity.com/paper/2201.01180/) (2021)
- [Stable Matching with Uncertain Linear Preferences](https://mlsanity.com/paper/1607.02917/) (2016)
- [The Effect of the Rooney Rule on Implicit Bias in the Long Term](https://mlsanity.com/paper/2010.10992/) (2020)
- [Two-Sided Matching Meets Fair Division](https://mlsanity.com/paper/2107.07404/) (2021)
- [On Fairness and Stability in Two-Sided Matchings](https://mlsanity.com/paper/2111.10885/) (2021)
- [Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints](https://mlsanity.com/paper/2202.01661/) (2022)
- [Maximizing Submodular Functions for Recommendation in the Presence of Biases](https://mlsanity.com/paper/2305.02806/) (2023)
- [Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions](https://mlsanity.com/paper/2306.09835/) (2023)
- [Bias in Evaluation Processes: An Optimization-Based Model](https://mlsanity.com/paper/2310.17489/) (2023)

## Abstract (arXiv)

This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the institutions, and the goal is to select candidates for each institution in a way that maximizes utility while also considering the candidates' preferences. The paper focuses on the setting in which candidates are divided into multiple groups and the observed utilities of candidates in some groups are biased--systematically lower than their true utilities. The first result is that, in these biased settings, prior algorithms can lead to selections with sub-optimal true utility and significant discrepancies in the fraction of candidates from each group that get their preferred choices. Subsequently, an algorithm is presented along with proof that it produces selections that achieve near-optimal group fairness with respect to preferences while also nearly maximizing the true utility under distributional assumptions. Further, extensive empirical validation of these results in real-world and synthetic settings, in which the distributional assumptions may not hold, are presented.