# Statistical Discrimination in Ratings-Guided Markets

arXiv: https://arxiv.org/abs/2004.11531 · HTML: https://mlsanity.com/paper/2004.11531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Interventions Against Machine-Assisted Statistical Discrimination](https://mlsanity.com/paper/2310.04585/) (2023)

## Abstract (arXiv)

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.