Towards Interpretable Hate Speech Detection using Large Language
  Model-extracted Rationales
Ayushi Nirmal Amrita Bhattacharjee Paras Sheth Huan Liu
Although social media platforms are a prominent arena for users to engage in
interpersonal discussions and express opinions, the facade and anonymity
offered by social media may allow users to spew hate speech and offensive
content. Given the massive scale of such platforms, there arises a need to
automatically identify and flag instances of hate speech. Although several hate
speech detection methods exist, most of these black-box methods are not
interpretable or explainable by design. To address the lack of
interpretability, in this paper, we propose to use state-of-the-art Large
Language Models (LLMs) to extract features in the form of rationales from the
input text, to train a base hate speech classifier, thereby enabling faithful
interpretability by design. Our framework effectively combines the textual
understanding capabilities of LLMs and the discriminative power of
state-of-the-art hate speech classifiers to make these classifiers faithfully
interpretable. Our comprehensive evaluation on a variety of social media hate
speech datasets demonstrate: (1) the goodness of the LLM-extracted rationales,
and (2) the surprising retention of detector performance even after training to
ensure interpretability.