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Computing & AI · Source-linked note

Buolamwini and Gebru: Gender Shades

Evaluated three commercial gender-classification systems with an intersectional benchmark and documented substantially different error rates across gender and skin-type groups.

What is recorded here?

Evaluated three commercial gender-classification systems with an intersectional benchmark and documented substantially different error rates across gender and skin-type groups.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

The record is the exact two-author 2018 paper and the systems, benchmark and demographic categories it tested. It does not establish the behavior of every face-analysis system, every demographic taxonomy or later product versions.

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People and systems

Joy Buolamwini; Timnit Gebru

The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.

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Documented countries / regions: United States

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English catalogue narrative

Buolamwini and Gebru: Gender Shades

Evaluated three commercial gender-classification systems with an intersectional benchmark and documented substantially different error rates across gender and skin-type groups.

Scientific scope

The record is the exact two-author 2018 paper and the systems, benchmark and demographic categories it tested. It does not establish the behavior of every face-analysis system, every demographic taxonomy or later product versions.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Joy Buolamwini; Timnit Gebru

The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.

Original sources