Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference.

ABSTRACT:

Objective

We recently showed that genderize.io is not a sufficiently powerful gender detection tool due to a large number of nonclassifications. In the present study, we aimed to assess whether the accuracy of inference by genderize.io can be improved by manipulating the first names in the database.

Methods

We used a database containing the first names, surnames, and gender of 6,131 physicians practicing in a multicultural country (Switzerland). We uploaded the original CSV file (file #1), the file obtained after removing all diacritic marks, such as accents and cedilla (file #2), and the file obtained after removing all diacritic marks and retaining only the first term of the compound first names (file #3). For each file, we computed three performance metrics: proportion of

SUBMITTER: Sebo P 

PROVIDER: S-EPMC8608220 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature