Rethinking Legal Compliance Automation: Opportunities with Large Language Models
CoRR(2024)
摘要
As software-intensive systems face growing pressure to comply with laws and
regulations, providing automated support for compliance analysis has become
paramount. Despite advances in the Requirements Engineering (RE) community on
legal compliance analysis, important obstacles remain in developing accurate
and generalizable compliance automation solutions. This paper highlights some
observed limitations of current approaches and examines how adopting new
automation strategies that leverage Large Language Models (LLMs) can help
address these shortcomings and open up fresh opportunities. Specifically, we
argue that the examination of (textual) legal artifacts should, first, employ a
broader context than sentences, which have widely been used as the units of
analysis in past research. Second, the mode of analysis with legal artifacts
needs to shift from classification and information extraction to more
end-to-end strategies that are not only accurate but also capable of providing
explanation and justification. We present a compliance analysis approach
designed to address these limitations. We further outline our evaluation plan
for the approach and provide preliminary evaluation results based on data
processing agreements (DPAs) that must comply with the General Data Protection
Regulation (GDPR). Our initial findings suggest that our approach yields
substantial accuracy improvements and, at the same time, provides justification
for compliance decisions.
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