Title: Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Fairness, Explainability, Hate speech detection

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.

TL;DR: We study whether, and in what ways, input-based explanations can be used to detect biased predictions, select fair models, and mitigate biases during model training.