Discriminating Tastes: Uber's Customer Ratings as Vehicles for Workplace Discrimination

Abstract

Consumer-sourced rating systems are a dominant method of worker evaluation in platform-based work. These systems facilitate the semi-automated management of large, disaggregated workforces, and the rapid growth of service platforms — but may also represent a potential avenue for employment discrimination that negatively impacts members of legally protected groups. We analyze the Uber platform as a case study to explore how bias may creep into evaluations of drivers through consumer-sourced rating systems, and draw on social science research to demonstrate how such bias emerges in other types of rating and evaluation systems. While companies are legally prohibited from making employment decisions based on protected characteristics of workers, their reliance on potentially biased consumer ratings to make material determinations may nonetheless lead to a disparate impact in employment outcomes. We analyze the limitations of current civil rights law to address this issue, and outline a number of operational, legal, and design-based interventions that might assist in so doing.

Keywords: platforms, data, discrimination, bias, inequality, ratings, sharing economy, algorithm