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Name-based demographic inference and the unequal distribution of misrecognition

Author

Listed:
  • Jeffrey W. Lockhart

    (University of Chicago)

  • Molly M. King

    (Santa Clara University)

  • Christin Munsch

    (University of Connecticut)

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.

Suggested Citation

  • Jeffrey W. Lockhart & Molly M. King & Christin Munsch, 2023. "Name-based demographic inference and the unequal distribution of misrecognition," Nature Human Behaviour, Nature, vol. 7(7), pages 1084-1095, July.
    • Handle: RePEc:nat:nathum:v:7:y:2023:i:7:d:10.1038_s41562-023-01587-9
    DOI: 10.1038/s41562-023-01587-9
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    Cited by:

    1. Carolina Biliotti & Luca Verginer & Massimo Riccaboni, 2024. "The Uneven Access to COVID-19 Research for Women in Science," Papers 2404.04707, arXiv.org, revised Oct 2024.
    2. Tampise Klebo, Daniska & Malancu, Natalia Cornelia & Ruedin, Didier, 2023. "Estimating the Number of Afro-Descendants in Switzerland," SocArXiv wytuj, Center for Open Science.
    3. Nakajima, Kazuki & Liu, Ruodan & Shudo, Kazuyuki & Masuda, Naoki, 2023. "Quantifying gender imbalance in East Asian academia: Research career and citation practice," Journal of Informetrics, Elsevier, vol. 17(4).

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