AI-Enabled Regulatory Change Analysis of Legal Requirements

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as legislation develops over time - new regulation can be introduced, while existing regulation can be amended, or repealed.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

Full description

Saved in:
Bibliographic Details
Main Authors: Abualhaija, Sallam, Ceci, Marcello, Sannier, Nicolas, Bianculli, Domenico, Briand, Lionel C., Zetzsche, Dirk, Bodellini, Marco
Format: Conference Proceeding
Language:English
Subjects:
Automation
ChatGPT
Large Language Models (LLMs)
Law
Legislation
Natural Language Processing (NLP)
Navigation
Pipelines
Prompt Engineering
Regulatory Change
Regulatory Compliance
Software systems
Taxonomy
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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as legislation develops over time - new regulation can be introduced, while existing regulation can be amended, or repealed.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.
ISSN:2332-6441
DOI:10.1109/RE59067.2024.00012