Statistical Discrimination in Ratings-Guided Markets
Computer Science and Game Theory
2024-11-11 v2 Theoretical Economics
Abstract
We study statistical discrimination of individuals based on payoff-irrelevant social identities in markets that utilize ratings and recommendations for social learning. Even though rating/recommendation algorithms can be designed to be fair and unbiased, ratings-based social learning can still lead to discriminatory outcomes. Our model demonstrates how users' attention choices can result in asymmetric data sampling across social groups, leading to discriminatory inferences and potential discrimination based on group identities.
Cite
@article{arxiv.2004.11531,
title = {Statistical Discrimination in Ratings-Guided Markets},
author = {Yeon-Koo Che and Kyungmin Kim and Weijie Zhong},
journal= {arXiv preprint arXiv:2004.11531},
year = {2024}
}