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

Conference papers

• Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, Chara Podimata (alpha order). Can Probabilistic Feedback Drive User Impacts in Online Platforms? AISTATS 2024. PDF
• Jessica Dai, Paula Gradu, Chris Harshaw (alpha order). Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments. NeurIPS 2023 (spotlight). PDF
• Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen H. Bach, Himabindu Lakkaraju. Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. 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. 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); Workshop on Machine Learning for Economic Policy at NeurIPS 2020; Women in Machine Learning Workshop at NeurIPS 2020 (contributed talk). PDF Video

Workshop papers

• Kweku Kwegyir-Aggrey, A. Feder Cooper, Jessica Dai, John P. Dickerson, Keegan Hines, Suresh Venkatasubramanian. Repairing Regressors for Fair Binary Classification at Any Decision Threshold. Workshop on Algorithmic Fairness through the Lens of Time at NeurIPS 2023 (oral presentation; PMLR proceedings). PDF
• Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju. What will it take to generate fairness-preserving explanations? 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. Workshop on Consequential Decisions in Dynamic Environments at NeurIPS 2020; Women in Machine Learning Workshop at NeurIPS 2020. PDF