Adversarial Scrubbing of Demographic Information for Text Classification
This addresses fairness issues in text classification by scrubbing demographic information, though it is incremental as it builds on existing adversarial debiasing methods.
The paper tackles the problem of language models encoding undesirable demographic attributes by introducing an adversarial learning framework, Adversarial Scrubber (ADS), which debiases contextual representations while maintaining target task performance, as shown in experimental evaluations on 8 datasets.
Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task. We aim to scrub such undesirable attributes and learn fair representations while maintaining performance on the target task. In this paper, we present an adversarial learning framework "Adversarial Scrubber" (ADS), to debias contextual representations. We perform theoretical analysis to show that our framework converges without leaking demographic information under certain conditions. We extend previous evaluation techniques by evaluating debiasing performance using Minimum Description Length (MDL) probing. Experimental evaluations on 8 datasets show that ADS generates representations with minimal information about demographic attributes while being maximally informative about the target task.