ACL Findings2021
On the Ethical Limits of Natural Language Processing on Legal Text
Dimitrios Tsarapatsanis, Nikolaos Aletras
Abstract
Natural language processing (NLP) methods for analyzing legal text offer legal scholars and practitioners a range of tools allowing to empirically analyze law on a large scale. However, researchers seem to struggle when it comes to identifying ethical limits to using NLP systems for acquiring genuine insights both about the law and the systems' predictive capacity. In this paper we set out a number of ways in which to think systematically about such issues. We place emphasis on three crucial normative parameters which have, to the best of our knowledge, been underestimated by current debates: (a) the importance of academic freedom, (b) the existence of a wide diversity of legal and ethical norms domestically but even more so internationally and (c) the threat of moralism in research related to computational law. For each of these three parameters we provide specific recommendations for the legal NLP community. Our discussion is structured around the study of a real-life scenario that has prompted recent debate in the legal NLP research community.
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Cited by top-tier papers5
- LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model DevelopmentIlias Chalkidis, Nicolas Garneau, Catalina Goanta, Daniel Martin Katz et al.ACL 2023 · 29 citations
- Regulation and NLP (RegNLP): Taming Large Language ModelsCatalina Goanta, Nikolaos Aletras, Ilias Chalkidis, Sofia Ranchordás et al.EMNLP 2023 · 4 citations
- LexGLUE: A Benchmark Dataset for Legal Language Understanding in EnglishIlias Chalkidis, Abhik Jana, Dirk Hartung, Michael J. Bommarito II et al.ACL 2022
- MultiLegalPile: A 689GB Multilingual Legal CorpusJoel Niklaus, Veton Matoshi, Matthias Stürmer, Ilias Chalkidis et al.ACL 2024
- FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text ProcessingIlias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada et al.ACL 2022
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