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Title: Bias Challenges in Counterfactual Data Augmentation
Deep learning models tend not to be out-of-distribution robust primarily due to their reliance on spurious features to solve the task. Counterfactual data augmentations provide a general way of (approximately) achieving representations that are counterfactual-invariant to spurious features, a requirement for out-of-distribution (OOD) robustness. In this work, we show that counterfactual data augmentations may not achieve the desired counterfactual-invariance if the augmentation is performed by a context-guessing machine, an abstract machine that guesses the most-likely context of a given input. We theoretically analyze the invariance imposed by such counterfactual data augmentations and describe an exemplar NLP task where counterfactual data augmentation by a context-guessing machine does not lead to robust OOD classifiers.  more » « less
Award ID(s):
1943364 1918483
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Uncertainty in artificial intelligence
Medium: X
Sponsoring Org:
National Science Foundation
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