# Multi-winner Approval Voting Goes Epistemic

arXiv: https://arxiv.org/abs/2201.06655 · HTML: https://mlsanity.com/paper/2201.06655/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [Approval Voting and Incentives in Crowdsourcing](https://mlsanity.com/paper/1502.05696/) (2015)
- [Evaluating approval-based multiwinner voting in terms of robustness to noise](https://mlsanity.com/paper/2002.01776/) (2020)
- [Surprisingly Popular Voting Recovers Rankings, Surprisingly!](https://mlsanity.com/paper/2105.09386/) (2021)
- [On Aggregation in Ensembles of Multilabel Classifiers](https://mlsanity.com/paper/2006.11916/) (2020)
- [Truth-tracking via Approval Voting: Size Matters](https://mlsanity.com/paper/2112.04387/) (2021)

## Built on by (most cited first, 2 of 2)

- [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)
- [AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence](https://mlsanity.com/paper/2605.16291/) (2026)

## Abstract (arXiv)

Epistemic voting interprets votes as noisy signals about a ground truth. We consider contexts where the truth consists of a set of objective winners, knowing a lower and upper bound on its cardinality. A prototypical problem for this setting is the aggre-gation of multi-label annotations with prior knowledge on the size of the ground truth. We posit noisemodels, for which we define rules that output an optimal set of winners. We report on experiments on multi-label annotations (which we collected).