Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information

• URL: http://arxiv.org/abs/2403.09516v3
• Date: Fri, 5 Apr 2024 18:35:37 GMT
• Title: Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information
• Authors: Shadi Iskander, Kira Radinsky, Yonatan Belinkov,
• Abstract summary: DAFair is a novel approach to address social bias in language models. We leverage prototypical demographic texts and incorporate a regularization term during the fine-tuning process to mitigate bias.
• Score: 50.29934517930506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mitigating social biases typically requires identifying the social groups associated with each data sample. In this paper, we present DAFair, a novel approach to address social bias in language models. Unlike traditional methods that rely on explicit demographic labels, our approach does not require any such information. Instead, we leverage predefined prototypical demographic texts and incorporate a regularization term during the fine-tuning process to mitigate bias in the model's representations. Our empirical results across two tasks and two models demonstrate the effectiveness of our method compared to previous approaches that do not rely on labeled data. Moreover, with limited demographic-annotated data, our approach outperforms common debiasing approaches.

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