Adversarial Scrubbing of Demographic Information for Text Classification

Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier Oliva, Shashank Srivastava, Snigdha Chaturvedi

Abstract:

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.