# Fairly Allocating Utility in Constrained Multiwinner Elections

arXiv: https://arxiv.org/abs/2211.12820 · HTML: https://mlsanity.com/paper/2211.12820/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Measuring Fairness in Ranked Outputs](https://mlsanity.com/paper/1610.08559/) (2016)
- [Ranking with Fairness Constraints](https://mlsanity.com/paper/1704.06840/) (2017)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Balanced Ranking with Diversity Constraints](https://mlsanity.com/paper/1906.01747/) (2019)
- [Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness](https://mlsanity.com/paper/1907.13158/) (2019)
- [Multiwinner Elections with Diversity Constraints](https://mlsanity.com/paper/1711.06527/) (2017)
- [Group Fairness in Committee Selection](https://mlsanity.com/paper/1905.04438/) (2019)
- [Achieving Fully Proportional Representation: Approximability Results](https://mlsanity.com/paper/1312.4026/) (2013)
- [Fair Division via Social Comparison](https://mlsanity.com/paper/1611.06589/) (2016)
- [Multi-Attribute Proportional Representation](https://mlsanity.com/paper/1509.03389/) (2015)
- [District-Fair Participatory Budgeting](https://mlsanity.com/paper/2102.06115/) (2021)
- [DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections](https://mlsanity.com/paper/2107.07356/) (2021)
- [Algorithmic Techniques for Necessary and Possible Winners](https://mlsanity.com/paper/2005.06779/) (2020)

## Abstract (arXiv)

Fairness in multiwinner elections is studied in varying contexts. For instance, diversity of candidates and representation of voters are both separately termed as being fair. A common denominator to ensure fairness across all such contexts is the use of constraints. However, across these contexts, the candidates selected to satisfy the given constraints may systematically lead to unfair outcomes for historically disadvantaged voter populations as the cost of fairness may be borne unequally. Hence, we develop a model to select candidates that satisfy the constraints fairly across voter populations. To do so, the model maps the constrained multiwinner election problem to a problem of fairly allocating indivisible goods. We propose three variants of the model, namely, global, localized, and inter-sectional. Next, we analyze the model's computational complexity, and we present an empirical analysis of the utility traded-off across various settings of our model across the three variants and discuss the impact of Simpson's paradox using synthetic datasets and a dataset of voting at the United Nations. Finally, we discuss the implications of our work for AI and machine learning, especially for studies that use constraints to guarantee fairness.