12 citations · 33 across the 9 of their papers we have counts for
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cs.LG2022
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…
cs.IR2022
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…
cs.CL2022★ 1 cited
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…