2018 CE
Gender Shades audits unequal error rates
Artificial intelligence · Bias and society
Joy Buolamwini and Timnit Gebru found substantial differences across gender and skin-tone groups in commercial classifiers. The study evaluated binary gender classification from faces, not every form of facial recognition, and made intersectional auditing a central evaluation concern.
Precision, calendar, and review
- Start
- year · no uncertainty qualifier in this record
- Calendar and convention
- Historical-era years; the source calendar is not identified. No conversion of days is assumed.
- Year zero
- The scale goes from 1 BCE to 1 CE without year zero. Changing era names changes labels only.
- Documentary review
- 2026-09-20
Reading the date: point event.
Sources
- proceedings.mlr.press (opens in a new tab)
Source provenance and scope
- Publication date
- 2018
- What this date describes
- The cited source or edition; it may postdate the historical event.
- Bibliographic date checked
- 2026-09-23
- Access date
- 2026-09-20
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