Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?

• URL: http://arxiv.org/abs/2509.22291v1
• Date: Fri, 26 Sep 2025 12:53:20 GMT
• Title: Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?
• Authors: Yifan Wang, Mayank Jobanputra, Ji-Ung Lee, Soyoung Oh, Isabel Valera, Vera Demberg,
• Abstract summary: We conduct the first systematic study of the relationship between explainability and fairness in hate speech detection.<n>We examine three key dimensions: identifying biased predictions, (2) selecting fair models, and (3) mitigating bias during model training.<n>Our findings show that input-based explanations can effectively detect biased predictions, but they are unreliable for selecting fair models among candidates.
• Score: 22.18673021255833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language processing (NLP) models often replicate or amplify social bias from training data, raising concerns about fairness. At the same time, their black-box nature makes it difficult for users to recognize biased predictions and for developers to effectively mitigate them. While some studies suggest that input-based explanations can help detect and mitigate bias, others question their reliability in ensuring fairness. Existing research on explainability in fair NLP has been predominantly qualitative, with limited large-scale quantitative analysis. In this work, we conduct the first systematic study of the relationship between explainability and fairness in hate speech detection, focusing on both encoder- and decoder-only models. We examine three key dimensions: (1) identifying biased predictions, (2) selecting fair models, and (3) mitigating bias during model training. Our findings show that input-based explanations can effectively detect biased predictions and serve as useful supervision for reducing bias during training, but they are unreliable for selecting fair models among candidates.

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