Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten. - Satyapriya Krishna; Jiaqi Ma 004; Himabindu Lakkaraju (ICML, 2023)

Post Hoc Explanations of Language Models Can Improve Language Models. - Satyapriya Krishna; Jiaqi Ma 004; Dylan Slack; Asma Ghandeharioun; Sameer Singh 001; Himabindu Lakkaraju (NeurIPS, 2023)

Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions. - Skyler Wu; Eric Meng Shen; Charumathi Badrinath; Jiaqi Ma 004; Himabindu Lakkaraju (CoRR, 2023)

Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage. - Catherine Huang; Chelse Swoopes; Christina Xiao; Jiaqi Ma 004; Himabindu Lakkaraju (CoRR, 2023)

Investigating the Fairness of Large Language Models for Predictions on Tabular Data. - Yanchen Liu; Srishti Gautam; Jiaqi Ma 004; Himabindu Lakkaraju (CoRR, 2023)

Fair Machine Unlearning: Data Removal while Mitigating Disparities. - Alex Oesterling; Jiaqi Ma 004; Flávio P. Calmon; Himabindu Lakkaraju (AISTATS, 2024)

Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications. - Yanchen Liu; Srishti Gautam; Jiaqi Ma 004; Himabindu Lakkaraju (NAACL-HLT, 2024)

OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning. - Jiaqi Ma 004; Vivian Lai; Yiming Zhang 022; Chacha Chen; Paul Hamilton; Davor Ljubenkov; Himabindu Lakkaraju; Chenhao Tan (CoRR, 2024)