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Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal

Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, and Aram Galstyan, in ACL Finding, 2022.

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Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However, these models can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. Therefore, knowledge distillation without any fairness constraints may preserve or exaggerate the teacher model’s biases onto the distilled model. To this end, we present a novel approach to mitigate gender disparity in text generation by learning a fair model during knowledge distillation. We propose two modifications to the base knowledge distillation based on counterfactual role reversal – modifying teacher probabilities and augmenting the training set. We evaluate gender polarity across professions in open-ended text generated from the resulting distilled and finetuned GPT-2models and demonstrate a substantial reduction in gender disparity with only a minor compromise in utility. Finally, we observe that language models that reduce gender polarity in language generation do not improve embedding fairness or downstream classification fairness.

Bib Entry

  title = {Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal},
  author = {Gupta, Umang and Dhamala, Jwala and Kumar, Varun and Verma, Apurv and Pruksachatkun, Yada and Krishna, Satyapriya and Gupta, Rahul and Chang, Kai-Wei and Steeg, Greg Ver and Galstyan, Aram},
  booktitle = {ACL Finding},
  year = {2022}

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