Fair Machine Learning Under Partial Compliance. - Jessica Dai; Sina Fazelpour; Zachary C. Lipton (AIES, 2021)

What will it take to generate fairness-preserving explanations? - Jessica Dai; Sohini Upadhyay; Stephen H. Bach; Himabindu Lakkaraju (CoRR, 2021)

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. - Jessica Dai; Sohini Upadhyay; Ulrich Aïvodji; Stephen H. Bach; Himabindu Lakkaraju (AIES, 2022)

Achieving Downstream Fairness with Geometric Repair. - Kweku Kwegyir-Aggrey; Jessica Dai; John P. Dickerson; Keegan Hines (CoRR, 2022)

CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments. - Jessica Dai; Paula Gradu; Christopher Harshaw (NeurIPS, 2023)

Can Probabilistic Feedback Drive User Impacts in Online Platforms? - Jessica Dai; Bailey Flanigan; Meena Jagadeesan; Nika Haghtalab; Chara Podimata (AISTATS, 2024)

Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis. - Jessica Dai (ICML, 2024)

Mapping Social Choice Theory to RLHF. - Jessica Dai; Eve Fleisig (CoRR, 2024)

Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis. - Jessica Dai (CoRR, 2024)

Learning With Multi-Group Guarantees For Clusterable Subpopulations. - Jessica Dai; Nika Haghtalab; Eric Zhao 003 (CoRR, 2024)

From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms. - Jessica Dai; Paula Gradu; Inioluwa Deborah Raji; Benjamin Recht (CoRR, 2025)

The Limits of Preference Data for Post-Training. - Eric Zhao 003; Jessica Dai; Pranjal Awasthi (CoRR, 2025)

Aggregated Individual Reporting for Post-Deployment Evaluation. - Jessica Dai; Inioluwa Deborah Raji; Benjamin Recht; Irene Y. Chen (CoRR, 2025)