much love to everyone I’ve gotten to work with 🙂

Preprints

• Jessica Dai, Paula Gradu, Chris Harshaw (alpha order). Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments. May 2023. PDF
• Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, Chara Podimata (alpha order). Can Probabilistic Feedback Drive User Impacts in Online Platforms? PDF forthcoming.

Conference papers

• Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen H. Bach, Himabindu Lakkaraju. Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. In AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2022. PDF
• Jessica Dai, Sina Fazelpour, Zachary C. Lipton. Fair Machine Learning Under Partial Compliance. In AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2021 (oral presentation). Previously in Workshop on Consequential Decisions in Dynamic Environments at NeurIPS 2020 (contributed talk); in Workshop on Machine Learning for Economic Policy at NeurIPS 2020; in Women in Machine Learning Workshop at NeurIPS 2020 (contributed talk). PDF Video

Workshop papers

• Jessica Dai, Kweku Kwegyir-Aggrey, John P. Dickerson, and Keegan Hines. Enabling Flexible Downstream Fairness with Geometric Repair. In Workshop on Responsible AI at KDD 2021. PDF
• Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju. What will it take to generate fairness-preserving explanations? In ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI 2021. PDF Poster
• Jessica Dai and Sarah M. Brown. Label Bias, Label Shift: Fair Machine Learning with Unreliable Labels. In Workshop on Consequential Decisions in Dynamic Environments at NeurIPS 2020; in Women in Machine Learning Workshop at NeurIPS 2020. PDF