12 citations · 33 across the 9 of their papers we have counts for
9 papers
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
James Atwood, Tina Tian, Ben Packer +5
Despite the rich literature on machine learning fairness, relatively little attention has been paid to remediating complex systems, where the final prediction is the combination of…
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals
Ananth Balashankar, Xuezhi Wang, Yao Qin +5
Counterfactual Data Augmentation (CDA) is a commonly used technique for improving robustness in natural language classifiers. However, one fundamental challenge is how to discover…
Striving for data-model efficiency: Identifying data externalities on group performance
Esther Rolf, Ben Packer, Alex Beutel +1
Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predict…
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Flavien Prost, Ben Packer, Jilin Chen +9
There has been a flurry of research in recent years on notions of fairness in ranking and recommender systems, particularly on how to evaluate if a recommender allocates exposure e…
Flexible text generation for counterfactual fairness probing
Zee Fryer, Vera Axelrod, Ben Packer +3
A common approach for testing fairness issues in text-based classifiers is through the use of counterfactuals: does the classifier output change if a sensitive attribute in the inp…
Causally motivated Shortcut Removal Using Auxiliary Labels
Maggie Makar, Ben Packer, Dan Moldovan +3
Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning. We study a flexible, causally-motiv…