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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
1442021
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
462022
Policy impacts of statistical uncertainty and privacy
R Steed, T Liu, ZS Wu, A Acquisti
Science 377 (6609), 928-931, 2022
172022
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
8*2021
AI auditing: The broken bus on the road to AI accountability
A Birhane, R Steed, V Ojewale, B Vecchione, ID Raji
arXiv preprint arXiv:2401.14462, 2024
42024
Learning to Live with Privacy-Preserving Analytics
A Acquisti, R Steed
Communications of the ACM 66 (7), 24-27, 2023
32023
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
12024
Managing the risks of inevitably biased visual artificial intelligence systems
A Caliskan, R Steed
Brookings Institution, 2022
12022
Heuristic-Based Weak Learning for Automated Decision-Making
R Steed, B Williams
Workshop on Participatory Machine Learning at ICML 2020, 2020
12020
Adoption of "Privacy-Preserving" Analytics: Drivers, Designs, & Decoupling
R Steed, A Acquisti
Available at SSRN 4718865, 2024
2024
Response to comment on “Policy impacts of statistical uncertainty and privacy”
R Steed, A Acquisti, ZS Wu, T Liu
Science 380 (6648), eadh2297, 2023
2023
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