Joy Buolamwini

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 271 researchers and is trusted by 332 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 52 citations into it; #2,249 of 21,575 by trust here, #1,875 by citations
• AI Ethics, Fairness & Society — 24 citations into it; #405 of 494 by trust here, #464 by citations
• Artificial Intelligence — 22 citations into it
• Safety, Robustness & Interpretability — 19 citations into it; #104 of 2,251 by trust here, #152 by citations
• Safety Research — 18 citations into it; #1,319 of 5,829 by trust here, #1,193 by citations
• Sociology and Political Science — 14 citations into it; #5,895 of 22,756 by trust here, #6,533 by citations
• Evaluation & Benchmarks — 13 citations into it
• Signal Processing — 13 citations into it

Whom Joy Buolamwini trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Markus Mathias
2. Mei Ngan
3. P. Jonathon Phillips
4. Sorelle A. Friedler
5. Tony Doyle
6. Ira Kemelmacher-Shlizerman
7. Rodrigo Benenson
8. Brendan Klare
9. Patrick Grother
10. Geoffrey E. Hinton

Who trusts Joy Buolamwini overall

1. Inioluwa Deborah Raji
2. Kimmo Kärkkäinen
3. Morgan Klaus Scheuerman
4. Ben Hutchinson
5. Emily L. Denton
6. Emilio Ferrara
7. Margaret Mitchell
8. Ethan Perez
9. Luke Stark
10. Jungseock Joo

Papers from Joy Buolamwini's perspective

Ordered by how strongly the researchers Joy Buolamwini trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018) — 5,467 citations
• Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing (2020) — 334 citations
• Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products (2019) — 692 citations
• Gender shades : intersectional phenotypic and demographic evaluation of face datasets and gender classifiers (2017) — 103 citations