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        <title>Legally Enforceable Hate Speech Detection for Public Forums</title>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.730</identifier>
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%0 Conference Proceedings
%T Legally Enforceable Hate Speech Detection for Public Forums
%A Luo, Chu
%A Bhambhoria, Rohan
%A Dahan, Samuel
%A Zhu, Xiaodan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F luo-etal-2023-legally
%X 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.
%R 10.18653/v1/2023.findings-emnlp.730
%U https://aclanthology.org/2023.findings-emnlp.730
%U https://doi.org/10.18653/v1/2023.findings-emnlp.730
%P 10948-10963

Markdown (Informal)

[Legally Enforceable Hate Speech Detection for Public Forums](https://aclanthology.org/2023.findings-emnlp.730) (Luo et al., Findings 2023)

• Legally Enforceable Hate Speech Detection for Public Forums (Luo et al., Findings 2023)

ACL

• Chu Luo, Rohan Bhambhoria, Samuel Dahan, and Xiaodan Zhu. 2023. Legally Enforceable Hate Speech Detection for Public Forums. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10948–10963, Singapore. Association for Computational Linguistics.