TY - GEN
T1 - Designing committees for mitigating biases
AU - Feldman, Michal
AU - Mansour, Yishay
AU - Nisan, Noam
AU - Oren, Sigal
AU - Tennenholtz, Moshe
N1 - Publisher Copyright: Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85099879778&partnerID=8YFLogxK
M3 - منشور من مؤتمر
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 1942
EP - 1949
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Y2 - 7 February 2020 through 12 February 2020
ER -