From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law

TL;DR

This paper investigates how Large Language Models can interpret and translate privacy consent laws into technical specifications, aiming to automate compliance checks and improve legal adherence in software development.

Contribution

It introduces a novel pipeline using LLMs for classifying and modifying software requirements to meet privacy consent standards, bridging legal and technical domains.

Findings

LLMs show potential in automating compliance tasks

Limitations exist in LLM reasoning capabilities

Benchmarking reveals strengths and weaknesses of LLMs in legal compliance

Abstract

Privacy law and regulation have turned to "consent" as the legitimate basis for collecting and processing individuals' data. As governments have rushed to enshrine consent requirements in their privacy laws, such as the California Consumer Privacy Act (CCPA), significant challenges remain in understanding how these legal mandates are operationalized in software. The opaque nature of software development processes further complicates this translation. To address this, we explore the use of Large Language Models (LLMs) in requirements engineering to bridge the gap between legal requirements and technical implementation. This study employs a three-step pipeline that involves using an LLM to classify software use cases for compliance, generating LLM modifications for non-compliant cases, and manually validating these changes against legal standards. Our preliminary findings highlight the potential of LLMs in automating compliance tasks, while also revealing limitations in their reasoning capabilities. By benchmarking LLMs against real-world use cases, this research provides insights into leveraging AI-driven solutions to enhance legal compliance of software.