Document Type
Article
Publication Date
4-17-2023
Publisher
Springer Nature Portfolio
Abstract
Academics and companies increasingly draw on large datasets to understand the social world, and name-based demographic ascription tools are widespread for imputing information that is often missing from these large datasets. These approaches have drawn criticism on ethical, empirical and theoretical grounds. Using a survey of all authors listed on articles in sociology, economics and communication journals in 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 found 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.
Recommended Citation
Lockhart, J. W., King, M. M., & Munsch, C. (2023). Name-based demographic inference and the unequal distribution of misrecognition. Nature Human Behaviour, 7(7), 1084–1095. https://doi.org/10.1038/s41562-023-01587-9
Included in
Feminist, Gender, and Sexuality Studies Commons, Social Justice Commons, Sociology Commons
Comments
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s41562-023-01587-9