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Name-based demographic inference and the unequal distribution of misrecognition
Jeffrey W Lockhart1, Molly M King2, Christin Munsch3
1Department of Sociology, University of Chicago, Chicago, IL, USA. jlockhart@uchicago.edu.
Nature Human Behaviour
|April 17, 2023
Summary
Name-based demographic imputation tools inaccurately misgender and misrepresent race/ethnicity for scholars. Researchers must exercise caution with these tools due to significant empirical and ethical concerns.
Area of Science:
- Social sciences research methodology
- Computational social science
- Data ethics in academia
Background:
- Large datasets are increasingly used in social sciences.
- Name-based demographic ascription tools are common for imputing missing data.
- These imputation methods face ethical, empirical, and theoretical criticism.
Purpose of the Study:
- To evaluate the accuracy of name-based demographic imputation tools.
- To compare self-identified demographics with imputed data for scholars.
- To identify inequalities in imputation errors across different demographic traits.
Main Methods:
- Survey of 19,924 scholars across sociology, economics, and communication journals (2015-2020).
- Comparison of self-reported gender and race/ethnicity against four gender and four race/ethnicity ascription tools.
- Analysis of imputation errors and their distribution across demographic categories.
Main Results:
- Substantial inequalities were found in how name-based tools misgender authors.
- Significant inaccuracies were observed in race/ethnicity ascription.
- Erroneous imputations were unevenly distributed among scholars with different demographic traits.
Conclusions:
- Name-based demographic imputation tools exhibit significant inaccuracies.
- Scholars must be cautious due to the empirical and ethical consequences of these errors.
- Five principles for responsible use of name-based demographic inference are recommended.
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