  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.
