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AI-enabled regulatory change analysis of legal requirements
Date
2024
Abstract
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as legislation develops over time – new regulation can be introduced, while existing regulation can be amended, or repealed.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.
Supervisor
Description
Publisher
Institute of Electrical and Electronics Engineers
Citation
2024 IEEE 32nd International Requirements Engineering Conference (RE), Reykjavik, Iceland, 2024, pp. 5-17
Files
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Briand_2024_AI.pdf
Adobe PDF, 1015.58 KB
Funding code
Funding Information
This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant reference NCER22/IS/16570468/NCER-FT. For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to,any Author Accepted Manuscript version arising from this submission. Lionel Briand was partially supported by the Science Foundation Ireland grant 13/RC/2094-2, and the Canada Research Chair and Discovery Grant programs of the Natural Sciences and Engineering Research Council of Canada (NSERC)