Reference : Avoiding bias when inferring race using name-based approaches
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Social & behavioral sciences, psychology : Sociology & social sciences
Computational Sciences
http://hdl.handle.net/10993/46829
Avoiding bias when inferring race using name-based approaches
English
Kozlowski, Diego mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Murray, Dakota S. [Indiana University Bloomington, IN, USA > School of Informatics, Computing, and Engineering]
Bell, Alexis [Berry College, GA, USA > Campbell School of Business]
Husley, Will [Berry College, GA, USA > Campbell School of Business]
Larivière, Vincent [Université de Montréal, Montréal, QC, Canada > École de bibliothéconomie et des sciences de l’information]
Monroe-White [Berry College, GA, USA > Campbell School of Business, > Assistant Professor of Technology, Entrepreneurship, and Data Analytics]
Sugimoto, Cassidy R. [Indiana University Bloomington, IN, USA > School of Informatics, Computing, and Engineering]
Jul-2021
18th INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS, 12–15 July 2021KU Leuven, Belgium
597-608
Yes
No
International
9789080328228
Belgium
18th International Conference on Scientometrics & Informetrics
from 12-07-2021 to 15-07-2021
ISSI
Leuven
Belgium
[en] Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, few large-scale analyses have been performed on this topic, mostly because of the lack of robust race-disambiguation algorithms. Identifying author information does not generally include the author’s race. Therefore, an algorithm needs to be employed, using known information about authors, i.e., their names, to infer their perceived race. Nevertheless, as any other algorithm, the process of racial inference can generate biases if it is not carefully considered. When the research is focused on the understanding of racial-based inequalities, such biases undermine the objectives of the investigation and may perpetuate inequities. The goal of this article is to assess the biases introduced by the different approaches used name-based racial inference. We use information from US census and mortgage applications to infer the race of US author names in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name-based inference varies by race and ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article fills an important research gap that will allow more systematic and unbiased studies on racial disparity in science.
University of Luxembourg
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/46829
https://issi2021.org/proceedings/
FnR ; FNR12252781 > Andreas Zilian > DRIVEN > Data-driven Computational Modelling And Applications > 01/09/2018 > 28/02/2025 > 2017

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