Testing for racial bias using inconsistent perceptions of race (2409.11269v1)

Abstract: Tests for racial bias commonly assess whether two people of different races are treated differently. A fundamental challenge is that, because two people may differ in many ways, factors besides race might explain differences in treatment. Here, we propose a test for bias which circumvents the difficulty of comparing two people by instead assessing whether the $\textit{same person}$ is treated differently when their race is perceived differently. We apply our method to test for bias in police traffic stops, finding that the same driver is likelier to be searched or arrested by police when they are perceived as Hispanic than when they are perceived as white. Our test is broadly applicable to other datasets where race, gender, or other identity data are perceived rather than self-reported, and the same person is observed multiple times.

• The paper introduces an innovative method that compares how the same individual is treated under different racial perceptions to directly measure bias.
• The analysis of traffic stop data revealed a 0.4 percentage point increase in search probability—about 24% of the average rate—when perceived as Hispanic.
• Robust sensitivity checks, including controls for location, time, and officer identity, validated the findings and highlighted critical implications for law enforcement policy.

An Analytical Overview of "Testing for racial bias using inconsistent perceptions of race"

The paper "Testing for racial bias using inconsistent perceptions of race" addresses a critical issue in quantifying racial bias in high-stakes decision-making processes. Traditional methods to detect racial bias involve comparing the treatment of individuals from different racial backgrounds. These methods are often confounded by additional variables that differ between individuals, complicating the attribution of disparate outcomes solely to racial bias.

Novel Methodological Approach

This work introduces an innovative approach that circumvents the challenge of comparing two different individuals by instead comparing how the same individual is treated when their race is perceived differently in different situations. This method leverages the fluid and inconsistent perceptions of race, as documented in various sociological studies. The core hypothesis is that if a single individual receives disparate treatment based on varying racial perceptions, this can directly indicate bias.

The paper applies this methodology to police traffic stop data from Arizona, Colorado, and Texas, where the same driver may be stopped multiple times, and their race perceived inconsistently. Approximately 17% of drivers were stopped more than once, and 9% of these had inconsistent racial perceptions across stops. The analysis focuses on drivers perceived alternatively as Hispanic or white, revealing that the same driver is more likely to be searched or arrested when perceived as Hispanic.

Key Findings and Implications

Empirical Validation

The paper employs a fixed effects linear probability model to assess the likelihood of police searches, including several robustness checks involving additional controls (e.g., officer identity, location, and time of the stop). Notably, the results showed a statistically significant 0.4 percentage point increase (approximately 24% of the average search rate) in the probability of being searched when perceived as Hispanic rather than white.

Sensitivity Analyses

To ensure the robustness of their findings, the authors conducted multiple sensitivity analyses. These include:

• Additional Controls: Introducing controls for stop location, time, and even officer identity, which yielded consistent results.
• Alternate Outcome Metrics: Examining arrest rates as an alternative measure of negative treatment, corroborating the bias findings.
• Outcome-Specific Models: Utilizing statistical models tailored for binary outcomes, such as fixed effects generalized linear models and conditional logistic regression, which similarly supported the primary conclusions.

These consistent findings across different methods and controls mitigate the concern that the results could be artifacts of model specification or unobserved confounds.

Theoretical and Practical Implications

From a theoretical standpoint, this approach advances the methodology for bias detection beyond traditional benchmarks that require extensive control for confounding variables. By isolating the effect of perceived race on treatment, the method provides a more direct measure of bias.

Practically, the paper reveals tangible biases in law enforcement behaviors that have significant implications for policy and training. For example, interventions could be designed to address implicit biases that cause disparate perceptions of race and thus differential treatment.

Future Directions

The method outlined has broader applicability beyond policing. It opens new avenues for examining bias in various settings, such as healthcare, education, and employment, where the same individual can be observed multiple times with varying perceived identities. Future developments could refine the methodology by exploring additional identity attributes (e.g., gender, age) and expanding datasets in different sociocultural contexts.

Conclusion

The paper provides a rigorous, nuanced analysis of racial bias through a novel lens of inconsistent racial perceptions. By demonstrating the robustness and applicability of the method in a real-world policing context, this work paves the way for more accurate and insightful investigations into bias in various domains. It significantly contributes to both the theoretical framework and practical tools available for addressing racial bias in high-stakes decisions.