Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Impact in
- Safety Research 538
Classified as
- Authors
- Joy BuolamwiniTimnit Gebru
In The Last Decade
doi.org/w57365196 →Countries where authors are citing Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
This map shows the geographic impact of Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification more than expected).
Fields of papers citing Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
This network shows the impact of Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.
About Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
This paper, published in 2018, received 1.6k indexed citations . Written by Joy Buolamwini and Timnit Gebru covering the research area of Gender Studies. It is primarily cited by scholars working on Safety Research (538 citations), Artificial Intelligence (521 citations), Sociology and Political Science (258 citations), Computer Vision and Pattern Recognition (215 citations) and Cognitive Neuroscience (170 citations).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w57365196.