Paper published in a book (Scientific congresses, symposiums and conference proceedings)
AI-enabled Regulatory Change Analysis of Legal Requirements
ABUALHAIJA, Sallam; CECI, Marcello; SANNIER, Nicolas et al.
2024In Proceedings of the 32nd IEEE International Requirements Engineering Conference (RE'24)
Peer reviewed
 

Files


Full Text
ACSBBZB-RE24.pdf
Author postprint (1.04 MB) Creative Commons License - Attribution
Download

All documents in ORBilu are protected by a user license.

Send to



Details



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as legislation develops over time – new regulation can be introduced, while existing regulation can be amended, or repealed.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

Statistics


Number of views
335 (40 by Unilu)
Number of downloads
292 (7 by Unilu)

Scopus citations®
 
0
Scopus citations®
without self-citations
0
OpenCitations
 
0

Bibliography


Similar publications



Contact ORBilu