ArticlePloS one2022
Avoiding bias when inferring race using name-based approaches.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Article
- The power of citation: giving credit where credit is due.Disease models & mechanisms · 2026Article
- Intersectional biases in narratives produced by open-ended prompting of generative language models.Nature communications · 2026Article
- Most researchers would receive more recognition if assessed by article-level metrics than by journal-level metrics.PLoS biology · 2025Article
- Comparing Multiple Imputation Methods to Address Missing Patient Demographics in Immunization Information Systems: Retrospective Cohort Study.JMIR public health and surveillance · 2025Article
- Gender and racial diversity socialization in science.Nature computational science · 2025Article
- Enabling disaggregation of Asian American subgroups: a dataset of Wikidata names for disparity estimation.Scientific data · 2025Article
- Article
- Unveiling the ethical void: Bias in reference citations and its academic ramifications.Current research in physiology · 2024Article
- Methods for retrospectively improving race/ethnicity data quality: a scoping review.Epidemiologic reviews · 2023Article
- Investigating the Influence of Assessment Question Framing on Undergraduate Biology Student Preference and Affect.CBE life sciences education · 2023Article
- Time ratio disparities among ED patients undergoing head CT.Emergency radiology · 2023Article
- Name-based demographic inference and the unequal distribution of misrecognition.Nature human behaviour · 2023Article
- Equal accuracy for Andrew and Abubakar-detecting and mitigating bias in name-ethnicity classification algorithms.AI & society · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, because of the lack of robust information on authors' race, few large-scale analyses have been performed on this topic. Algorithmic approaches offer one solution, using known information about authors, such as their names, to infer their perceived race. As with any other algorithm, the process of racial inference can generate biases if it is not carefully considered. The goal of this article is to assess the extent to which algorithmic bias is introduced using different approaches for name-based racial inference. We use information from the U.S. Census and mortgage applications to infer the race of U.S. affiliated authors 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/ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article lays the foundation for more systematic and less-biased investigations into racial disparities in science.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.