  We study statistical discrimination of individuals based on payoff-irrelevant
social identities in markets where ratings/recommendations facilitate social
learning among users. Despite the potential promise and guarantee for the
ratings/recommendation algorithms to be fair and free of human bias and
prejudice, we identify the possible vulnerability of the ratings-based social
learning to discriminatory inferences on social groups. In our model, users'
equilibrium attention decisions may lead data to be sampled differentially
across different groups so that differential inferences on individuals may
emerge based on their group identities. We explore policy implications in terms
of regulating trading relationships as well as algorithm design.
