Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference
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...
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| Format: | Article |
| Language: | English |
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University Library System, University of Pittsburgh
2021-11-01
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| Series: | Journal of the Medical Library Association |
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| Online Access: | https://jmla.pitt.edu/ojs/jmla/article/view/1252 |
| _version_ | 1818938550628909056 |
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| author | Paul Sebo |
| author_facet | Paul Sebo |
| author_sort | Paul Sebo |
| collection | DOAJ |
| description | 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 misclassifications (errorCodedWithoutNA), proportion of nonclassifications (naCoded), and proportion of misclassifications and nonclassifications (errorCoded).
Results: naCoded, which was high for file #1 (16.4%), was reduced after data manipulation (file #2: 11.7%, file #3: 0.4%). As the increase in the number of misclassifications was small, the overall performance of genderize.io (i.e., errorCoded) improved, especially for file #3 (file #1: 17.7%, file #2: 13.0%, and file #3: 2.3%).
Conclusions: A relatively simple manipulation of the data improved the accuracy of gender inference by genderize.io. We recommend using genderize.io only with files that were modified in this way.
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| first_indexed | 2024-12-20T06:09:38Z |
| format | Article |
| id | doaj.art-4e0663cfa18448c28fb87b6ae0702cc7 |
| institution | Directory Open Access Journal |
| issn | 1536-5050 1558-9439 |
| language | English |
| last_indexed | 2024-12-20T06:09:38Z |
| publishDate | 2021-11-01 |
| publisher | University Library System, University of Pittsburgh |
| record_format | Article |
| series | Journal of the Medical Library Association |
| spelling | doaj.art-4e0663cfa18448c28fb87b6ae0702cc72022-12-21T19:50:43ZengUniversity Library System, University of PittsburghJournal of the Medical Library Association1536-50501558-94392021-11-01109410.5195/jmla.2021.1252608Using genderize.io to infer the gender of first names: how to improve the accuracy of the inferencePaul SeboObjective: 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 misclassifications (errorCodedWithoutNA), proportion of nonclassifications (naCoded), and proportion of misclassifications and nonclassifications (errorCoded). Results: naCoded, which was high for file #1 (16.4%), was reduced after data manipulation (file #2: 11.7%, file #3: 0.4%). As the increase in the number of misclassifications was small, the overall performance of genderize.io (i.e., errorCoded) improved, especially for file #3 (file #1: 17.7%, file #2: 13.0%, and file #3: 2.3%). Conclusions: A relatively simple manipulation of the data improved the accuracy of gender inference by genderize.io. We recommend using genderize.io only with files that were modified in this way. https://jmla.pitt.edu/ojs/jmla/article/view/1252accuracygender determinationgenderize.iomisclassificationnamename-to-gender |
| spellingShingle | Paul Sebo Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference Journal of the Medical Library Association accuracy gender determination genderize.io misclassification name name-to-gender |
| title | Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference |
| title_full | Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference |
| title_fullStr | Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference |
| title_full_unstemmed | Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference |
| title_short | Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference |
| title_sort | using genderize io to infer the gender of first names how to improve the accuracy of the inference |
| topic | accuracy gender determination genderize.io misclassification name name-to-gender |
| url | https://jmla.pitt.edu/ojs/jmla/article/view/1252 |
| work_keys_str_mv | AT paulsebo usinggenderizeiotoinferthegenderoffirstnameshowtoimprovetheaccuracyoftheinference |