Joy Buolamwini

What she did

With Timnit Gebru, published Gender Shades in 2018, measuring errors in three commercial gender-classification systems across skin-type and gender groups.

Impact and significance

The study found the highest error rates for darker-skinned women in the evaluated groups, showing why a single overall accuracy score can conceal disparities.

This is Matria’s interpretation, not a quotation or personalised advice.

Works recorded in the archive

• Gender Shades (2018, with Timnit Gebru)
• Founded the Algorithmic Justice League (2016)
• Unmasking AI (2023)

Sources and access

• Joy Buolamwini and Timnit Gebru. Gender Shades. PMLR 81:77-91, 2018.

  Abstract and pp. 77-91

• Buolamwini, Joy. Unmasking AI: My Mission to Protect What Is Human in a World of Machines. Random House, 2023.Bibliographic reference. Consult the named edition through a library or publisher.
• Buolamwini, Joy and Gebru, Timnit. Gender Shades. Proceedings of Machine Learning Research, 2018.Bibliographic reference. Consult the named edition through a library or publisher.

This label concerns the sources on this record. It does not validate every argument an author makes or establish what is true for a reader. Bibliographic coverage does not mean every passage or quotation has been verified. Specialist review remains pending.