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Racial bias, colorism, and overcorrection

Kenneth Colombe, Alex Krumer, Rosa Lavelle-Hill, Tim Pawlowski

TL;DR

This study investigates whether heightened awareness of racial bias affects bias and colorism in a non-discriminatory setting using the WNBA. It combines AI-driven race predictions with a continuous skin-tone measure from an image-to-data pipeline and a three-period natural experiment around heightened attention to bias, employing player-year and game fixed effects to identify effects of referee makeup on fouls. The main finding is that there was no significant racial bias pre-awareness, but post-awareness there is evidence of overcorrection, especially when skin tone differences are measured continuously; players facing more opposite-race referees earned fewer fouls, with effects diminishing in 2011–2014. These results imply that awareness-raising interventions can inadvertently induce out-group favoritism in environments with low baseline bias, underscoring the need to account for existing bias levels when designing DEI policies and their evaluations.

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.

Racial bias, colorism, and overcorrection

TL;DR

This study investigates whether heightened awareness of racial bias affects bias and colorism in a non-discriminatory setting using the WNBA. It combines AI-driven race predictions with a continuous skin-tone measure from an image-to-data pipeline and a three-period natural experiment around heightened attention to bias, employing player-year and game fixed effects to identify effects of referee makeup on fouls. The main finding is that there was no significant racial bias pre-awareness, but post-awareness there is evidence of overcorrection, especially when skin tone differences are measured continuously; players facing more opposite-race referees earned fewer fouls, with effects diminishing in 2011–2014. These results imply that awareness-raising interventions can inadvertently induce out-group favoritism in environments with low baseline bias, underscoring the need to account for existing bias levels when designing DEI policies and their evaluations.

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.

Paper Structure

This paper contains 19 sections, 2 equations, 11 figures, 34 tables.

Table of Contents

  1. Introduction
  2. The Women's National Basketball Association
  3. Officiating
  4. Social justice
  5. Methodology
  6. Data and measures
  7. Empirical strategy
  8. Results
  9. Binary race measure
  10. Continuous skin tone measure
  11. In-group discrimination versus out-group favoritism
  12. Individual referees
  13. Discussion
  14. Potential mechanisms
  15. Limitations
  16. ...and 4 more sections

Figures (11)

  • Figure 1: FairFace Racial Predictions
  • Figure 2: Sample Distribution of L*, a*, and b* values
  • Figure 3: Image-to-data pipeline
  • Figure 4: Full sample WNBA faces sorted by L*
  • Figure 5: Locally weighted smoothing and marginal effects of crew distance on foul rate
  • ...and 6 more figures