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 language | American English |
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Title of host publication | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence |
Publisher | AAAI Press |
Pages | 1942-1949 |
Number of pages | 8 |
ISBN (Electronic) | 9781577358350 |
State | Published - 2020 |
Event | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States Duration: 7 Feb 2020 → 12 Feb 2020 |
Publication series
Name | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence |
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Conference
Conference | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 |
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Country/Territory | United States |
City | New York |
Period | 7/02/20 → 12/02/20 |
Bibliographical note
Funding Information:This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 740282 and 740435), the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) (grant agreement No 337122), and by the Israel Science Foundation (grant numbers 317/17, 993/17 and 2167/19).
Funding Information:
∗This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 740282 and 740435), the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) (grant agreement No 337122), and by the Israel Science Foundation (grant numbers 317/17, 993/17 and 2167/19). Copyright ©c 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Publisher Copyright:
Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.