  Hate speech causes widespread and deep-seated societal issues. Proper
enforcement of hate speech laws is key for protecting groups of people against
harmful and discriminatory language. However, determining what constitutes hate
speech is a complex task that is highly open to subjective interpretations.
Existing works do not align their systems with enforceable definitions of hate
speech, which can make their outputs inconsistent with the goals of regulators.
This research introduces a new perspective and task for enforceable hate speech
detection centred around legal definitions, and a dataset annotated on
violations of eleven possible definitions by legal experts. Given the challenge
of identifying clear, legally enforceable instances of hate speech, we augment
the dataset with expert-generated samples and an automatically mined challenge
set. We experiment with grounding the model decision in these definitions using
zero-shot and few-shot prompting. We then report results on several large
language models (LLMs). With this task definition, automatic hate speech
detection can be more closely aligned to enforceable laws, and hence assist in
more rigorous enforcement of legal protections against harmful speech in public
forums.
