Publications

Below we provide links to published research from the hub on the use of Machine Learning [ML] models for policy purposes and the struggle to govern AI. If you are unable to access a paper, contact the authors and they will send you a copy.

• Cao, Y., Domingo, L.F., Gilbert, S.A., Mazurek, M, Shilton, K., & Daume, H. “Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators through a User-Centric Method.” The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024).
• Winecoff, A. & Bogen, M (2024, September). Improving governance outcomes through AI documentation: Bridging theory and practice. Center for Democracy & Technology.
• Winecoff, A. & Bogen, M (2024, March 6). Trustworthy AI needs trustworthy measurements. Center for Democracy & Technology.
• Winecoff, A. A., & Watkins, E. A. (2022, July). Artificial concepts of artificial intelligence: Institutional compliance and resistance in AI startups. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (pp. 788-799).
• Winecoff, A. A., Brasoveanu, F., Casavant, B., Washabaugh, P., & Graham, M. (2019, September). Users in the loop: A psychologically-informed approach to similar item retrieval. In Proceedings of the 13th ACM conference on recommender systems (pp. 52-59).