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.

翻译：本文研究在利用评分与推荐进行社会学习的市场中，基于与收益无关的社会身份对个体产生的统计性歧视。尽管评分/推荐算法可被设计为公平且无偏，但基于评分的社会学习仍可能导致歧视性结果。我们的模型揭示了用户注意力选择如何导致跨社会群体的不对称数据采样，进而引发基于群体身份的歧视性推断与潜在歧视。