Timnit Gebru—Co-authored Gender Shades research exposing racial and gender bias in facial recognition systems
Gebru co-authored the landmark Gender Shades study with Joy Buolamwini at MIT, which found that commercial facial recognition systems had error rates of over 34% for darker-skinned women compared to less than 1% for lighter-skinned men. The research led to significant industry changes, including Microsoft retiring gender classification in Azure Face API and IBM discontinuing general-purpose facial recognition.
Scoring Impact
| Topic | Direction | Relevance | Contribution |
|---|---|---|---|
| Algorithmic Fairness | +toward | primary | +1.00 |
| Gender Equity | +toward | secondary | +0.50 |
| Racial Justice | +toward | secondary | +0.50 |
| Overall incident score = | +0.680 | ||
Score = avg(topic contributions) × significance (high ×1.5) × confidence (0.68)
Evidence (2 signals)
MIT study found gender and skin-type bias in commercial AI systems
MIT News reported on the Gender Shades research by Joy Buolamwini and Timnit Gebru, which found commercial facial recognition systems had error rates of over 34% for darker-skinned women compared to less than 1% for lighter-skinned men.
Gender Shades project documented at gendershades.org
The Gender Shades project website documents the full research findings on intersectional accuracy disparities in commercial gender classification systems by Buolamwini and Gebru.