CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. - Tianlu Wang; Xuezhi Wang 002; Yao Qin 001; Ben Packer; Kang Li; Jilin Chen; Alex Beutel; Ed H. Chi (EMNLP (1), 2020)

Can We Improve Model Robustness through Secondary Attribute Counterfactuals? - Ananth Balashankar; Xuezhi Wang 002; Ben Packer; Nithum Thain; Ed H. Chi; Alex Beutel (EMNLP (1), 2021)

Causally motivated shortcut removal using auxiliary labels. - Maggie Makar; Ben Packer; Dan Moldovan; Davis W. Blalock; Yoni Halpern; Alexander D'Amour (AISTATS, 2022)

Flexible text generation for counterfactual fairness probing. - Zee Fryer; Vera Axelrod; Ben Packer; Alex Beutel; Jilin Chen; Kellie Webster (CoRR, 2022)

Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations. - Flavien Prost; Ben Packer; Jilin Chen; Li Wei; Pierre Kremp; Nick Blumm; Susan Wang; Tulsee Doshi; Tonia Osadebe; Lukasz Heldt; Ed H. Chi; Alex Beutel (CoRR, 2022)

Striving for data-model efficiency: Identifying data externalities on group performance. - Esther Rolf; Ben Packer; Alex Beutel; Fernando Diaz 001 (CoRR, 2022)

Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation. - Ananth Balashankar; Xuezhi Wang 002; Yao Qin 001; Ben Packer; Nithum Thain; Ed H. Chi; Jilin Chen; Alex Beutel (EMNLP (Findings), 2023)

Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting. - Preethi Lahoti; Nicholas Blumm; Xiao Ma 010; Raghavendra Kotikalapudi; Sahitya Potluri; Qijun Tan; Hansa Srinivasan; Ben Packer; Ahmad Beirami; Alex Beutel; Jilin Chen (EMNLP, 2023)

Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals. - Ananth Balashankar; Xuezhi Wang 002; Yao Qin 001; Ben Packer; Nithum Thain; Jilin Chen; Ed H. Chi; Alex Beutel (CoRR, 2023)

Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification. - James Atwood; Tina Tian; Ben Packer; Meghana Deodhar; Jilin Chen; Alex Beutel; Flavien Prost; Ahmad Beirami (CoRR, 2023)

FRAPPÉ: A Post-Processing Framework for Group Fairness Regularization. - Alexandru Tifrea; Preethi Lahoti; Ben Packer; Yoni Halpern; Ahmad Beirami; Flavien Prost (CoRR, 2023)

FRAPPÉ: A Group Fairness Framework for Post-Processing Everything. - Alexandru Tifrea; Preethi Lahoti; Ben Packer; Yoni Halpern; Ahmad Beirami; Flavien Prost (ICML, 2024)