In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes. Amidst the steady flow of research solutions stemming from the NLP domain, the study of use cases has fallen behind, leading to a number of innovative technical methods without a place in practice. In this work, we aim to build a structured overview of Legal Tech use cases, grounded in NLP literature, but also supplemented by voices from legal practice in Germany. Based upon a Systematic Literature Review, we identify seven categories of NLP technologies for the legal domain, which are then studied in juxtaposition to 22 legal use cases. In the investigation of these use cases, we identify 15 ethical, legal, and social aspects (ELSA), shedding light on the potential concerns of digitally transforming the legal domain.
@article{arxiv.2404.18759,
title = {Towards A Structured Overview of Use Cases for Natural Language Processing in the Legal Domain: A German Perspective},
author = {Juraj Vladika and Stephen Meisenbacher and Martina Preis and Alexandra Klymenko and Florian Matthes},
journal= {arXiv preprint arXiv:2404.18759},
year = {2024}
}
Comments
10 pages, 6 tables, 30th Americas Conference on Information Systems (AMCIS 2024)