TY - GEN

T1 - Applying NLP to Support Legal Decision-making in Administrative Appeal Boards in the EU

AU - Olsen, Henrik Palmer

AU - Højmark-Bertelsen, Malte

AU - Schwemer, Sebastian Felix

N1 - Publisher Copyright: © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

PY - 2023

Y1 - 2023

N2 - While Natural Language Processing (NLP) is being applied in an increasing number of contexts, including law, it remains a difficult task to leverage NLP for the purpose of real-life support of legal decision-making. This is because 1) legal-decision making must be made in a way that is sensitive not only to legislation but also to evolving case practice (prior decision-making that functions as precedent), 2) legal-decision making is sensitive to open-ended legislative language and shifting factual contexts, 3) traditional methods of NLP are capable of processing long texts, but they are suboptimal compared to novel methods, i.e., transformer-based models, e.g., BERT [1], etc. 4) however the transformer-based models are limited by maximum input lengths, which makes it difficult to apply in real-life scenarios, where legal documents exceed the maximum input length. In this paper, we show how we tackle the problem of providing NLP-based intelligence support to legal decision-makers in a real-world setting using transformer-based NLP.

AB - While Natural Language Processing (NLP) is being applied in an increasing number of contexts, including law, it remains a difficult task to leverage NLP for the purpose of real-life support of legal decision-making. This is because 1) legal-decision making must be made in a way that is sensitive not only to legislation but also to evolving case practice (prior decision-making that functions as precedent), 2) legal-decision making is sensitive to open-ended legislative language and shifting factual contexts, 3) traditional methods of NLP are capable of processing long texts, but they are suboptimal compared to novel methods, i.e., transformer-based models, e.g., BERT [1], etc. 4) however the transformer-based models are limited by maximum input lengths, which makes it difficult to apply in real-life scenarios, where legal documents exceed the maximum input length. In this paper, we show how we tackle the problem of providing NLP-based intelligence support to legal decision-makers in a real-world setting using transformer-based NLP.

KW - automation bias

KW - decision support

KW - legal decision-making

KW - Legal information retrieval

KW - NLP

KW - public administration

M3 - Conference article

AN - SCOPUS:85167803037

VL - 3441

SP - 103

EP - 110

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 6th Workshop on Automated Semantic Analysis of Information in Legal Text, ASAIL 2023

Y2 - 23 September 2023

ER -