# DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections

arXiv: https://arxiv.org/abs/2107.07356 · HTML: https://mlsanity.com/paper/2107.07356/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 83 references that are in the map.
- First posted on arXiv 15 Jul 2021.

## Builds on (most cited first, 24 of 24)

- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [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)
- [Justified Representation in Approval-Based Committee Voting](https://mlsanity.com/paper/1407.8269/) (2014)
- [Proportional Justified Representation](https://mlsanity.com/paper/1611.09928/) (2016)
- [Properties of Multiwinner Voting Rules](https://mlsanity.com/paper/1506.02891/) (2015)
- [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)
- [On the Computation of Fully Proportional Representation](https://mlsanity.com/paper/1402.0580/) (2014)
- [Multiwinner Elections with Diversity Constraints](https://mlsanity.com/paper/1711.06527/) (2017)
- [Fair Knapsack](https://mlsanity.com/paper/1711.04520/) (2017)
- [Achieving Fully Proportional Representation: Approximability Results](https://mlsanity.com/paper/1312.4026/) (2013)
- [Structure in Dichotomous Preferences](https://mlsanity.com/paper/1505.00341/) (2015)
- [Consistent Approval-Based Multi-Winner Rules](https://mlsanity.com/paper/1704.02453/) (2017)
- [Multiwinner Approval Rules as Apportionment Methods](https://mlsanity.com/paper/1611.08691/) (2016)
- [Multi-Attribute Proportional Representation](https://mlsanity.com/paper/1509.03389/) (2015)
- [District-Fair Participatory Budgeting](https://mlsanity.com/paper/2102.06115/) (2021)
- [Dynamic Proportional Rankings](https://mlsanity.com/paper/2105.08043/) (2021)
- [Parameterized Complexity of Multi-winner Determination: More Effort Towards Fixed-Parameter Tractability](https://mlsanity.com/paper/2107.04685/) (2021)
- [Group Fairness for Knapsack Problems](https://mlsanity.com/paper/2006.07832/) (2020)
- [On the complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules](https://mlsanity.com/paper/2004.13933/) (2020)
- [Algorithmic Techniques for Necessary and Possible Winners](https://mlsanity.com/paper/2005.06779/) (2020)

## Built on by (most cited first, 3 of 3)

- [Temporal Fairness in Multiwinner Voting](https://mlsanity.com/paper/2312.04417/) (2023)
- [Fairly Allocating Utility in Constrained Multiwinner Elections](https://mlsanity.com/paper/2211.12820/) (2022)
- [On the Complexity of Finding a Diverse and Representative Committee using a Monotone, Separable Positional Multiwinner Voting Rule](https://mlsanity.com/paper/2211.13217/) (2022)

## Abstract (arXiv)

The study of fairness in multiwinner elections focuses on settings where candidates have attributes. However, voters may also be divided into predefined populations under one or more attributes (e.g., "California" and "Illinois" populations under the "state" attribute), which may be same or different from candidate attributes. The models that focus on candidate attributes alone may systematically under-represent smaller voter populations. Hence, we develop a model, DiRe Committee WinnerDetermination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule. We analyze its computational complexity, inapproximability, and parameterized complexity. We develop a heuristic-based algorithm, which finds the winning DiRe committee in under two minutes on 63% of the instances of synthetic datasets and on 100% of instances of real-world datasets. We present an empirical analysis of the running time, feasibility, and utility traded-off. Overall, DRCWD motivates that a study of multiwinner elections should consider both its actors, namely candidates and voters, as candidate-specific models can unknowingly harm voter populations, and vice versa. Additionally, even when the attributes of candidates and voters coincide, it is important to treat them separately as diversity does not imply representation and vice versa. This is to say that having a female candidate on the committee, for example, is different from having a candidate on the committee who is preferred by the female voters, and who themselves may or may not be female.