Racial bias, colorism, and overcorrection

TL;DR

This study investigates the impact of heightened awareness on racial bias and colorism in sports officiating, revealing evidence of overcorrection following media coverage, with implications for diversity policies.

Contribution

It introduces a novel empirical approach using AI and machine learning to measure skin tone and race, analyzing bias dynamics in a real-world setting with a natural experiment.

Findings

No significant racial bias before media coverage

Evidence of overcorrection in refereeing decisions after coverage

Overcorrection diminishes over time

Abstract

This paper examines whether increased awareness can affect racial bias and colorism. We exploit a natural experiment arising from the widespread publicity of Price and Wolfers (2010), which served as an external shock, intensifying scrutiny of racial bias in men's basketball officiating. We investigate refereeing decisions in a similar setting, the Women's National Basketball Association (WNBA), which is known as a progressive institution with a longstanding commitment to diversity, equity, and inclusion (DEI) policy. We apply state-of-the-art artificial intelligence and machine learning techniques to systematically predict race and objectively measure skin tone. Our empirical strategy exploits the quasi-random assignment of referees to games, combined with high-dimensional fixed effects, to estimate the relationship between the racial and skin tone compositions of referees and players, as well as foul-calling behavior. Our results show no significant racial bias before the intense media coverage. However, afterward, we find evidence of overcorrection: a player earns fewer fouls when facing more referees from the opposite race and skin tone. Even though this overcorrection seems to wear off over time, we highlight the need to consider baseline levels of bias before applying any prescription with direct relevance to policymakers and organizations, given the recent discourse on DEI.