论文标题
评级指导市场中的统计歧视
Statistical Discrimination in Ratings-Guided Markets
论文作者
论文摘要
我们研究基于利用评级和建议的社会学习建议的回报的社会身份的个人的统计歧视。即使可以设计评分/建议算法是公平且公正的，但基于评分的社会学习仍然可以带来歧视性结果。我们的模型展示了用户的注意力选择如何导致社会群体之间的不对称数据采样，从而导致基于群体身份的歧视性推论和潜在的歧视。
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.
