Machines Learn Appearance Bias in Face Recognition. - Ryan Steed; Aylin Caliskan (CoRR, 2020)

Heuristic-Based Weak Learning for Moral Decision-Making. - Ryan Steed (CoRR, 2020)

Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases. - Ryan Steed; Aylin Caliskan (FAccT, 2021)

A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild. - Ryan Steed; Aylin Caliskan (AI Ethics, 2021)

Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models. - Ryan Steed; Swetasudha Panda; Ari Kobren; Michael L. Wick (ACL (1), 2022)

Learning to Live with Privacy-Preserving Analytics. - Alessandro Acquisti; Ryan Steed (Commun. ACM, 2023)

AI auditing: The Broken Bus on the Road to AI Accountability. - Abeba Birhane; Ryan Steed; Victor Ojewale; Briana Vecchione; Inioluwa Deborah Raji (SaTML, 2024)

Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing. - Ryan Steed; Diana Qing; Zhiwei Steven Wu (CoRR, 2024)

Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling. - Victor Ojewale; Ryan Steed; Briana Vecchione; Abeba Birhane; Inioluwa Deborah Raji (CHI, 2025)