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Designing committees for mitigating biases

  • Michal Feldman
  • , Yishay Mansour
  • , Noam Nisan
  • , Sigal Oren
  • , Moshe Tennenholtz

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Scopus citations

    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.

    Original languageEnglish
    Title of host publicationAAAI 2020 - 34th AAAI Conference on Artificial Intelligence
    PublisherAAAI press
    Pages1942-1949
    Number of pages8
    ISBN (Electronic)9781577358350
    StatePublished - 1 Jan 2020
    Event34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States
    Duration: 7 Feb 202012 Feb 2020

    Publication series

    NameAAAI 2020 - 34th AAAI Conference on Artificial Intelligence

    Conference

    Conference34th AAAI Conference on Artificial Intelligence, AAAI 2020
    Country/TerritoryUnited States
    CityNew York
    Period7/02/2012/02/20

    ASJC Scopus subject areas

    • Artificial Intelligence

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