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

The paper presents a systematic quantitative study examining how explainability methods relate to fairness in hate speech detection, focusing on three applications: identifying biased predictions, selecting fair models, and mitigating bias during training. It resides in the 'Explainability for Fairness Assessment and Auditing' leaf, which contains five papers total. This leaf sits within the broader 'Integrated Explainability-Fairness Studies' branch, indicating a moderately populated research direction that explicitly bridges transparency and equity concerns rather than treating them separately.

The taxonomy reveals neighboring work in 'Fairness-Aware Explainable Model Design' (three papers on joint optimization) and separate branches for pure explainability methods (fourteen papers across post-hoc, rationale-based, and concept-based techniques) and pure fairness analysis (seven papers on bias detection and mitigation). The paper's leaf focuses specifically on using explanations as diagnostic tools for fairness auditing, distinguishing it from sibling work like systematic auditing frameworks or individual biased prediction detection. This positioning reflects the field's evolution toward recognizing explainability and fairness as mutually reinforcing rather than independent dimensions.

Among thirty candidates examined, none clearly refute the three core contributions. The systematic study of the explainability-fairness relationship (ten candidates examined, zero refutable) appears novel in its comprehensive quantitative scope across encoder and decoder models. The quantitative evaluation framework for three fairness-explainability applications (ten candidates, zero refutable) and the empirical findings on explanation effectiveness (ten candidates, zero refutable) similarly show no substantial prior overlap within the limited search. The absence of refutable candidates suggests these contributions occupy relatively unexplored territory, though the modest search scale leaves open the possibility of relevant work beyond the top-thirty semantic matches.

Based on the limited literature search, the work appears to make substantive contributions to an active but not overcrowded research area. The taxonomy structure shows the field has established separate traditions for explainability and fairness, with the integrated branch representing a newer synthesis. The analysis covers top-thirty semantic matches and does not claim exhaustive coverage of all potentially relevant prior work in adjacent domains or earlier publication venues.