Designing Committees for Mitigating Biases
Michal Feldman, Yishay Mansour, Noam Nisan, Sigal Oren, Moshe Tennenholtz
Abstract
It is widely observed that individuals prefer to interact with others who are more similar to them (this phenomenon is termed homophily). This similarity manifests itself in various ways such as beliefs, values and education. Thus, it should not come as a surprise that when people make hiring choices, for example, their similarity to the candidate plays a role in their choice. In this paper, we suggest that putting the decision in the hands of a committee instead of a single person can reduce this bias. We study a novel model of voting in which a committee of experts is constructed to reduce the biases of its members. We first present voting rules that optimally reduce the biases of a given committee. Our main results include the design of committees, for several settings, that are able to reach a nearly optimal (unbiased) choice. We also provide a thorough analysis of the trade-offs between the committee size and the obtained error. Our model is inherently different from the well-studied models of voting that focus on aggregation of preferences or on aggregation of information due to the introduction of similarity biases.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext b5df9835-3233-430f-b797-e8fccb03abdcCited by top-tier papers1
Ask how each one uses itRelated papers
- On Mitigating Affinity Bias through Bandits with Evolving Biased FeedbackMatthew Faw, Constantine Caramanis, Jessica HoffmannICML 2025
- Favorite-Candidate Voting for Eliminating the Least Popular Candidate in a Metric SpaceXujin Chen, Minming Li, Chenhao WangAAAI 2020 · 16 citations
- Can We Predict the Election Outcome from Sampled Votes?Evi Micha, Nisarg ShahAAAI 2020 · 8 citations
- Modeling Voters in Multi-Winner Approval VotingJaelle Scheuerman, Jason L. Harman, Nicholas Mattei, K. Brent VenableAAAI 2021 · 4 citations
- How Many Representatives Do We Need? The Optimal Size of a Congress Voting on Binary IssuesManon Revel, Tao Lin, Daniel HalpernAAAI 2022 · 1 citation
