@inproceedings{zufall-etal-2022-legal,
title = "A Legal Approach to Hate Speech {--} Operationalizing the {EU}{'}s Legal Framework against the Expression of Hatred as an {NLP} Task",
author = "Zufall, Frederike and
Hamacher, Marius and
Kloppenborg, Katharina and
Zesch, Torsten",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preo{\textcommabelow{t}}iuc-Pietro, Daniel",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nllp-1.5",
doi = "10.18653/v1/2022.nllp-1.5",
pages = "53--64",
abstract = "We propose a {`}legal approach{'} to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law into an NLP task. Comparing existing regulatory regimes for hate speech, we base our investigation on the European Union{'}s framework as it provides a widely applicable legal minimum standard. Accurately deciding whether a post is punishable or not usually requires legal education. We show that, by breaking the legal assessment down into a series of simpler sub-decisions, even laypersons can annotate consistently. Based on a newly annotated dataset, our experiments show that directly learning an automated model of punishable content is challenging. However, learning the two sub-tasks of {`}target group{'} and {`}targeting conduct{'} instead of a holistic, end-to-end approach to the legal assessment yields better results. Overall, our method also provides decisions that are more transparent than those of end-to-end models, which is a crucial point in legal decision-making.",
}
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<abstract>We propose a ‘legal approach’ to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law into an NLP task. Comparing existing regulatory regimes for hate speech, we base our investigation on the European Union’s framework as it provides a widely applicable legal minimum standard. Accurately deciding whether a post is punishable or not usually requires legal education. We show that, by breaking the legal assessment down into a series of simpler sub-decisions, even laypersons can annotate consistently. Based on a newly annotated dataset, our experiments show that directly learning an automated model of punishable content is challenging. However, learning the two sub-tasks of ‘target group’ and ‘targeting conduct’ instead of a holistic, end-to-end approach to the legal assessment yields better results. Overall, our method also provides decisions that are more transparent than those of end-to-end models, which is a crucial point in legal decision-making.</abstract>
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%0 Conference Proceedings
%T A Legal Approach to Hate Speech – Operationalizing the EU’s Legal Framework against the Expression of Hatred as an NLP Task
%A Zufall, Frederike
%A Hamacher, Marius
%A Kloppenborg, Katharina
%A Zesch, Torsten
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%S Proceedings of the Natural Legal Language Processing Workshop 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F zufall-etal-2022-legal
%X We propose a ‘legal approach’ to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law into an NLP task. Comparing existing regulatory regimes for hate speech, we base our investigation on the European Union’s framework as it provides a widely applicable legal minimum standard. Accurately deciding whether a post is punishable or not usually requires legal education. We show that, by breaking the legal assessment down into a series of simpler sub-decisions, even laypersons can annotate consistently. Based on a newly annotated dataset, our experiments show that directly learning an automated model of punishable content is challenging. However, learning the two sub-tasks of ‘target group’ and ‘targeting conduct’ instead of a holistic, end-to-end approach to the legal assessment yields better results. Overall, our method also provides decisions that are more transparent than those of end-to-end models, which is a crucial point in legal decision-making.
%R 10.18653/v1/2022.nllp-1.5
%U https://aclanthology.org/2022.nllp-1.5
%U https://doi.org/10.18653/v1/2022.nllp-1.5
%P 53-64
Markdown (Informal)
[A Legal Approach to Hate Speech – Operationalizing the EU’s Legal Framework against the Expression of Hatred as an NLP Task](https://aclanthology.org/2022.nllp-1.5) (Zufall et al., NLLP 2022)
ACL