Nature Human Behavior

Academics and companies increasingly draw on large datasets to understand the social world, and name-based demographic ascription tools are widespread for imputing information like gender and race that are often missing from these large datasets. These approaches have drawn criticism on ethical, empirical, and theoretical grounds. Employing a survey of all authors listed on articles in sociology, economics, and communications journals in the Web of Science between 2015 and 2020, we compared self-identified demographics with name-based imputations of gender and race/ethnicity for 19,924 scholars across four gender ascription tools and four race/ethnicity ascription tools. We find substantial inequalities in how these tools misgender and misrecognize the race/ethnicity of authors, distributing erroneous ascriptions unevenly among other demographic traits. Because of the empirical and ethical consequences of these errors, scholars need to be cautious with the use of demographic imputation. We recommend five principles for the responsible use of name-based demographic inference.

Lockhart, Jeffrey W, Molly M. King, and Christin Munsch. 2023. “Name-based Demographic Inference and the Unequal Distribution of Misrecognition.” Nature Human Behavior 0(0):xx-xx. (Open Access) (Preprint)