Towards Legally Enforceable Hate Speech Detection for Public Forums

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 Figure 1: A visualization of our proposed method to challenge of identifying clear, legally enforceable ground hate speech to specialized legal definitions. A instances of hate speech, we augment the legal professional reads external legal resources and dataset with expert-generated samples and an makes a judgement on some hate speech input, then automatically mined challenge set. We experiment identifies offences according to our definitions and with grounding the model decision in makes a judgement on violations.