I’m especially excited when technical work can be informed by perspectives from sociology, political theory, philosophy, and other humanistic disciplines. 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. See also my Google Scholar.
much love to everyone I’ve gotten to work with 🙂
Conference papers
- Jessica Dai, Sean D.Garcia, Emma Pierson, Benjamin Recht, Nika Haghtalab. Three Years of r/ChatGPT: Societal Impact Evaluations from Social Media Data. ICML 2026. Also oral & outstanding paper award at AI4GOOD Workshop at ICML 2026. [PDF] [Website]
- Jessica Dai, Paula Gradu, Inioluwa Deborah Raji, Benjamin Recht. From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms. ICML 2025. [PDF] [Blog]
- Jessica Dai, Nika Haghtalab, Eric Zhao (alphabetical order). Learning With Multi-Group Guarantees For Clusterable Subpopulations. ICML 2025. [PDF]
- 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. AIES 2022. [PDF]
- Jessica Dai, Sina Fazelpour, Zachary C. Lipton. Fair Machine Learning Under Partial Compliance. AIES 2021 (oral). Also orals at WiML and Workshop on Consequential Decisions in Dynamic Environments at NeurIPS 2020. [PDF] [Talk]
Position papers
Jury’s out on whether position paper tracks were a good idea. That said, here are two that I’ve written about topics I care a lot about.
- Jessica Dai, Inioluwa Deborah Raji, Benjamin Recht, Irene Y. Chen. Aggregated Individual Reporting for Post-Deployment Evaluation. Position Paper, ICML 2026. [PDF]
- Jessica Dai. Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis. Position Paper, ICML 2024 (oral). [PDF] [Poster] [Talk]
Workshop papers
- Jessica Dai, Anwen Huang, Catherine Nasrallah, Rhiannon Croci, Hossein Soleimani, Sarah J. Pollet, Julia Adler-Milstein, Sara G. Murray, Jinoos Yazdany, Irene Y. Chen. Patient Safety Risks from AI Scribes: Signals from End-User Feedback.. Findings paper, Machine Learning for Health 2025. [PDF]
- 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]
Manuscripts
- Eric Zhao, Jessica Dai, Pranjal Awasthi. The Limits of Preference Data for Post-Training. [PDF] [Blog]