Avoiding bias when inferring race using name-based approaches

Name-based racial inference produces systematically different error rates across racial groups: The study found that the accuracy of inferring race from names varies significantly by race/ethnicity, meaning some groups are consistently misclassified more than others—a critical finding for anyone using this method to study diversity in academia.

Threshold-based methods significantly distort racial representation: When researchers use cutoff scores to make binary race classifications, the results underestimate Black authors while overestimating White authors, potentially masking or misrepresenting the very racial disparities these studies aim to uncover.

Methodological choices matter enormously: The paper demonstrates that decisions about whether to use given names, family names, continuous probability scores, or data imputation substantially affect results, suggesting that researchers must be transparent about their methods and acknowledge limitations when drawing conclusions about racial equity in science.

Name-based inference alone is insufficient for equity research: Because algorithmic bias is inherent in name-based approaches, the authors caution that this method should not be the sole basis for understanding racial disparities—implying that more robust data collection on author demographics may be necessary for accurate equity assessments.

Specific recommendations can reduce bias: The paper provides actionable guidance on how to minimize algorithmic bias in racial inference, offering a pathway for researchers to conduct more equitable large-scale analyses of diversity in academia despite current data limitations.