# Fair and Optimal Cohort Selection for Linear Utilities

arXiv: https://arxiv.org/abs/2102.07684 · HTML: https://mlsanity.com/paper/2102.07684/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Fairness Under Composition](https://mlsanity.com/paper/1806.06122/) (2018)
- [Downstream Effects of Affirmative Action](https://mlsanity.com/paper/1808.09004/) (2018)
- [Individual Fairness in Pipelines](https://mlsanity.com/paper/2004.05167/) (2020)
- [Fair Pipelines](https://mlsanity.com/paper/1707.00391/) (2017)
- [Pipeline Interventions](https://mlsanity.com/paper/2002.06592/) (2020)
- [Fair and Useful Cohort Selection](https://mlsanity.com/paper/2009.02207/) (2020)

## Built on by (most cited first, 1 of 1)

- [On Meritocracy in Optimal Set Selection](https://mlsanity.com/paper/2102.11932/) (2021)

## Abstract (arXiv)

The rise of algorithmic decision-making has created an explosion of research around the fairness of those algorithms. While there are many compelling notions of individual fairness, beginning with the work of Dwork et al., these notions typically do not satisfy desirable composition properties. To this end, Dwork and Ilvento introduced the fair cohort selection problem, which captures a specific application where a single fair classifier is composed with itself to pick a group of candidates of size exactly k. In this work we introduce a specific instance of cohort selection where the goal is to choose a cohort maximizing a linear utility function. We give approximately optimal polynomial-time algorithms for this problem in both an offline setting where the entire fair classifier is given at once, or an online setting where candidates arrive one at a time and are classified as they arrive.