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Ryan Steed
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Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
R Steed, A Caliskan
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021
1972021
Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models
R Steed, S Panda, A Kobren, M Wick
Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022
822022
SoK: AI Auditing: The Broken Bus on the Road to AI Accountability
A Birhane, R Steed, V Ojewale, B Vecchione, ID Raji
2nd IEEE Conference on Secure and Trustworthy Machine Learning, 2024
36*2024
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
V Ojewale, R Steed, B Vecchione, A Birhane, ID Raji
arXiv preprint arXiv:2402.17861, 2024
332024
Policy impacts of statistical uncertainty and privacy
R Steed, T Liu, ZS Wu, A Acquisti
Science 377 (6609), 928-931, 2022
332022
A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild
R Steed, A Caliskan
AI Ethics, 2021
7*2021
Algorithmic Decoupling and the Adoption of `Privacy-Preserving' Analytics
R Steed, A Acquisti
Available at SSRN 4718865, 2025
6*2025
Learning to live with privacy-preserving analytics
A Acquisti, R Steed
Communications of the ACM 66 (7), 24-27, 2023
22023
Heuristic-Based Weak Learning for Automated Decision-Making
R Steed, B Williams
Workshop on Participatory Machine Learning at ICML 2020, 2020
22020
Response to comment on “Policy impacts of statistical uncertainty and privacy”
R Steed, A Acquisti, ZS Wu, T Liu
Science 380 (6648), eadh2297, 2023
12023
Managing the risks of inevitably biased visual artificial intelligence systems
A Caliskan, R Steed
Brookings Institution, 2022
12022
Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing
R Steed, D Qing, ZS Wu
arXiv preprint arXiv:2407.04776, 2024
2024
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Artikler 1–12