TY - GEN
T1 - AI-Enabled Regulatory Change Analysis of Legal Requirements
AU - Abualhaija, Sallam
AU - Ceci, Marcello
AU - Sannier, Nicolas
AU - Bianculli, Domenico
AU - Briand, Lionel C.
AU - Zetzsche, Dirk
AU - Bodellini, Marco
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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as legislation develops over time - new regulation can be introduced, while existing regulation can be amended, or repealed.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.
AB - 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as legislation develops over time - new regulation can be introduced, while existing regulation can be amended, or repealed.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.
KW - ChatGPT
KW - Large Language Models (LLMs)
KW - Natural Language Processing (NLP)
KW - Prompt Engineering
KW - Regulatory Change
KW - Regulatory Compliance
UR - http://www.scopus.com/inward/record.url?scp=85202771036&partnerID=8YFLogxK
U2 - 10.1109/RE59067.2024.00012
DO - 10.1109/RE59067.2024.00012
M3 - Conference contribution
AN - SCOPUS:85202771036
T3 - Proceedings of the IEEE International Conference on Requirements Engineering
SP - 5
EP - 17
BT - Proceedings - 32nd IEEE International Requirements Engineering Conference, RE 2024
A2 - Liebel, Grischa
A2 - Hadar, Irit
A2 - Spoletini, Paola
PB - IEEE Computer Society
T2 - 32nd IEEE International Requirements Engineering Conference, RE 2024
Y2 - 24 June 2024 through 28 June 2024
ER -