Bias Challenges in Counterfactual Data Augmentation

S Chandra Mouli, Yangze Zhou, Bruno Ribeiro

Keywords: out-of-distribution robustness, counterfactual data augmentation

Abstract:

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 {\em 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.

TL;DR: Counterfactual data augmentations may not result in robust classifiers if the augmentation is performed by inferring the most-likely context of the input example.