I’ve had the privilege of working with Professors Hima Lakkaraju (Harvard), Steve Bach (Brown), Zachary Lipton (CMU), and Sarah M. Brown (URI). Generally curious about this mostly-broken thing we call machine learning.
- Jessica Dai, Sina Fazelpour, Zachary 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
- 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