I’m interested in how individuals, groups, and AI systems might interact with each other, and the {statistical, algorithmic, …} questions that such interactions raise. I think of theory as (i) a means to provide tools for addressing such problems, and (ii) a language for understanding these ideas at a conceptual level. I’m especially excited when technical work can be informed by perspectives from sociology, political theory, philosophy, and other humanistic disciplines.

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

Manuscripts

• Jessica Dai, Paula Gradu, Inioluwa Deborah Raji, Benjamin Recht. From Individual Experience to Collective Evidence: An Incident-Based Framework for Identifying Systemic Discrimination. [PDF]
• Jessica Dai, Nika Haghtalab, Eric Zhao (alphabetical order). Learning With Multi-Group Guarantees For Clusterable Subpopulations. [PDF]

Conference papers

• Jessica Dai. Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis. Position Paper, ICML 2024 (oral). [PDF] [Poster] [Talk]
• Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, Chara Podimata (alphabetical order). Can Probabilistic Feedback Drive User Impacts in Online Platforms? AISTATS 2024. [PDF] [Poster]
• Jessica Dai, Paula Gradu, Chris Harshaw (alphabetical order). Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments. NeurIPS 2023 (spotlight). [PDF] [INFORMS Slides]
• 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). Previously in Workshop on Consequential Decisions in Dynamic Environments at NeurIPS 2020 (oral); Workshop on Machine Learning for Economic Policy at NeurIPS 2020; Women in Machine Learning Workshop at NeurIPS 2020 (oral). [PDF] [Video]

Workshop papers

• Jessica Dai and Eve Fleisig (alphabetical order). Mapping Social Choice Theory to RLHF. Workshop on Reliable and Responsible Foundation Models at ICLR 2024. [PDF]
• 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; 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]