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Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification - researchr publication
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Foundational AI fairness paper demonstrating real-world bias in deployed commercial systems; critical reference for discussions of evaluation methodology, algorithmic accountability, and the social impacts of AI deployment.
Metadata
Importance: 82/100conference paperprimary source
Summary
Seminal 2018 study by Joy Buolamwini and Timnit Gebru auditing commercial facial analysis AI systems for accuracy disparities across gender and skin tone. The research found that darker-skinned females were misclassified at rates up to 34.7% higher than lighter-skinned males, exposing significant intersectional bias in deployed AI products from Microsoft, IBM, and Face++. This work became foundational in the AI fairness and algorithmic accountability movement.
Key Points
- •Commercial gender classifiers from major tech companies showed error rates up to 34.7% higher for darker-skinned women compared to lighter-skinned men
- •Introduced intersectional analysis combining race (skin tone) and gender, showing that examining each dimension separately obscures compounded disparities
- •Demonstrated that training datasets (like IJB-A and Adience) were heavily skewed toward lighter-skinned and male subjects
- •Directly prompted Microsoft, IBM, and others to improve their facial analysis APIs following the study's publication
- •Helped establish the field of algorithmic auditing as a methodology for evaluating real-world AI system fairness
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Deep Learning Revolution Era | Historical | 44.0 |
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Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification - researchr publication researchr explore Tags Journals Conferences Authors Profiles Groups calendar New Conferences Events Deadlines search search You are not signed in Sign in Sign up External Links DOI DBLP Google Google Scholar MSAS Cite Key BuolamwiniG18 Statistics References: 0 Cited by: 0 Reviews: 0 Bibliographies: 0 PDF [Upload PDF for personal use] Researchr Researchr is a web site for finding, collecting, sharing, and reviewing scientific publications, for researchers by researchers. Sign up for an account to create a profile with publication list, tag and review your related work, and share bibliographies with your co-authors. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification Joy Buolamwini , Timnit Gebru . Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification . In Sorelle A. Friedler , Christo Wilson , editors, Conference on Fairness, Accountability and Transparency, FAT 2018, 23-24 February 2018, New York, NY, USA . Volume 81 of Proceedings of Machine Learning Research , pages 77-91 , PMLR, 2018. [doi] Abstract Authors BibTeX References Bibliographies Reviews Related Abstract Abstract is missing. About Contact Credits Help Web Service API Blog FAQ Feedback runs on Web DSL
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0a7e3d48ddc5a853 | Stable ID: OTRhZGJjNz