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• W3201217479 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." @default.
• W3201217479 created "2021-09-27" @default.
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• W3201217479 date "2021-01-01" @default.
• W3201217479 modified "2023-09-25" @default.
• W3201217479 title "Adversarial Scrubbing of Demographic Information for Text Classification" @default.
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• W3201217479 doi "https://doi.org/10.18653/v1/2021.emnlp-main.43" @default.
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