Inferring gender from first names: Comparing the accuracy of Genderize, Gender API, and the gender R package on authors of diverse nationality

Alexander D VanHelene, Ishaani Khatri, C Beau Hilton, Sanjay Mishra, Ece D Gamsiz Uzun and Jeremy L Warner

PLOS Digital Health, 2024, vol. 3, issue 10, 1-15

Abstract: Meta-researchers commonly leverage tools that infer gender from first names, especially when studying gender disparities. However, tools vary in their accuracy, ease of use, and cost. The objective of this study was to compare the accuracy and cost of the commercial software Genderize and Gender API, and the open-source gender R package. Differences in binary gender prediction accuracy between the three services were evaluated. Gender prediction accuracy was tested on a multi-national dataset of 32,968 gender-labeled clinical trial authors. Additionally, two datasets from previous studies with 5779 and 6131 names, respectively, were re-evaluated with modern implementations of Genderize and Gender API. The gender inference accuracy of Genderize and Gender API were compared, both with and without supplying trialists’ country of origin in the API call. The accuracy of the gender R package was only evaluated without supplying countries of origin. The accuracy of Genderize, Gender API, and the gender R package were defined as the percentage of correct gender predictions. Accuracy differences between methods were evaluated using McNemar’s test. Genderize and Gender API demonstrated 96.6% and 96.1% accuracy, respectively, when countries of origin were not supplied in the API calls. Genderize and Gender API achieved the highest accuracy when predicting the gender of German authors with accuracies greater than 98%. Genderize and Gender API were least accurate with South Korean, Chinese, Singaporean, and Taiwanese authors, demonstrating below 82% accuracy. Genderize can provide similar accuracy to Gender API while being 4.85x less expensive. The gender R package achieved below 86% accuracy on the full dataset. In the replication studies, Genderize and gender API demonstrated better performance than in the original publications. Our results indicate that Genderize and Gender API achieve similar accuracy on a multinational dataset. The gender R package is uniformly less accurate than Genderize and Gender API.Author summary: Gender disparities in academia have prompted researchers to investigate gender gaps in professorship roles and publication authorship. Of particular concern are the gender gaps in cancer clinical trial authorship. Methodologies that evaluate gender disparities in academia often rely on tools that infer gender from first names. Tools that predict gender from first names are often used in methodologies that determine the gender ratios of academic departments or publishing authors in a discipline. However, researchers must choose between different gender predicting tools that vary in their accuracy, ease of use, and cost. We evaluated the binary gender prediction accuracy of Genderize, Gender API, and the gender R package on a gold-standard dataset of 32,968 clinical trialists from around the world. Genderize and Gender API are commercially available, while the gender R package is free and open source. We found that Genderize and Gender API were more accurate than the gender R package. In addition, Genderize is cheaper than Gender API, but is more sensitive to inconsistencies in name formatting and the presence of diacritical marks. Both Genderize and Gender API were most accurate with non-Asian names.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000456

DOI: 10.1371/journal.pdig.0000456

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