AI-enabled Regulatory Change Analysis of Legal Requirements

ABUALHAIJA, Sallam; CECI, Marcello; SANNIER, Nicolas; BIANCULLI, Domenico; Briand, Lionel; ZETZSCHE, Dirk Andreas; BODELLINI, Marco

doi:10.1109/RE59067.2024.00012

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AI-enabled Regulatory Change Analysis of Legal Requirements

ABUALHAIJA, Sallam; CECI, Marcello; SANNIER, Nicolas et al.

2024 • In Proceedings of the 32nd IEEE International Requirements Engineering Conference (RE'24)

Peer reviewed

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https://hdl.handle.net/10993/60766

DOI
10.1109/RE59067.2024.00012

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Keywords :

Regulatory Change; Prompt Engineering; Natural Language Processing (NLP); Large Language Models (LLMs); ChatGPT; Regulatory Compliance

Abstract :

[en] Statutory law is subject to change as legislation develops over time – new regulation can be introduced, while existing regulation can be amended, or repealed. From a requirements engineering (RE) perspective, such change must be dealt with to ensure the compliance of software systems at all times. Understanding the implications of regulatory change on compliance of software requirements requires navigating hundreds of legal provisions. Analyzing instances of regulatory change entirely manually is not only time-consuming, but also risky, since missing a change may result in non-compliant software which can in turn lead to hefty fines. In this paper, we propose MURCIA, an automated approach that leverages recent language models to assist human analysts in analyzing regulatory changes. To build MURCIA, we define a taxonomy that characterizes the regulatory changes at the textual level as well as the changes in the text’s meaning and legal interpretation. We evaluate MURCIA on four regulations from the financial domain. Over our evaluation set, MURCIA can identify textual changes with F1 score of 90.5%, and it can provide, according to our taxonomy, the text meaning and legal interpretation with an F1 score of 90.8% and 83.7%, respectively.

Research center :

Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SVV - Software Verification and Validation
NCER-FT - FinTech National Centre of Excellence in Research [LU]

Disciplines :

Computer science

Author, co-author :

ABUALHAIJA, Sallam ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV

CECI, Marcello ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV

SANNIER, Nicolas ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV

BIANCULLI, Domenico ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV

Briand, Lionel; Lero SFI centre for Software Research and University of Limerick ; University of Ottawa > School of EECS

ZETZSCHE, Dirk Andreas ; University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Department of Law (DL)

BODELLINI, Marco ; University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Department of Law (DL)

External co-authors :

yes

Language :

English

Title :

AI-enabled Regulatory Change Analysis of Legal Requirements

Publication date :

2024

Event name :

32nd IEEE International Requirements Engineering Conference

Event place :

Reykjavik, Iceland

Event date :

from 24 to 28 June, 2024

Audience :

International

Main work title :

Proceedings of the 32nd IEEE International Requirements Engineering Conference (RE'24)

Publisher :

IEEE

Peer reviewed :

Peer reviewed

FnR Project :

FNR16570468 - 2021 (01/07/2022-30/06/2030) - Yves Le Traon

Name of the research project :

U-AGR-7501 - NCER22/IS/16570468/NCER-FT_AFRICA_UL - BIANCULLI Domenico

Funders :

FNR - Luxembourg National Research Fund [LU]

Funding number :

NCER22/IS/16570468/NCER-FT

Available on ORBilu :

since 17 April 2024

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