Probing Classifiers are Unreliable for Concept Removal and Detection

Abhinav Kumar, Chenhao Tan, Amit Sharma

Keywords: Probing, Null-Space Removal, Adversarial Removal, Spurious Correlation, Fairness

Abstract: Neural network models trained on text data have been found to encode undesirable linguistic or sensitive concepts in their representation. Removing such concepts is non-trivial because of a complex relationship between the concept, text input, and the learnt representation. Recent work has proposed post-hoc and adversarial methods to remove such unwanted concepts from a model's representation. Through an extensive theoretical and empirical analysis, we show that these methods can be counter-productive: they are unable to remove the concepts entirely, and in the worst case may end up destroying all task-relevant features. The reason is the methods' reliance on a probing classifier as a proxy for the concept. Even under the most favorable conditions for learning a probing classifier when a concept's relevant features in representation space alone can provide 100% accuracy, we prove that a probing classifier is likely to use non-concept features and thus post-hoc or adversarial methods will fail to remove the concept correctly. These theoretical implications are confirmed by experiments on models trained on synthetic, Multi-NLI, and Twitter datasets. For sensitive applications of concept removal such as fairness, we recommend caution against using these methods and propose a spuriousness metric to gauge the quality of the final classifier.

TL;DR: We theoretically and experimentally demonstrate that even under favorable conditions, probing-based null-space and adversarial removal methods fail to remove the sensitive attribute from latent representation.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/probing-classifiers-are-unreliable-for/code)