ContextLens: Modeling Imperfect Privacy and Safety Context for Legal Compliance

2026-04-14

AI Summary

This work addresses the limitations of existing privacy and AI compliance assessment methods, which often assume complete contextual information despite real-world scenarios frequently involving ambiguity or missing context. To bridge this gap, the paper introduces ContextLens, a novel framework that—without requiring model training—integrates rule-based reasoning with large language models through semi-formalized inference to guide structured responses to legal compliance queries. ContextLens explicitly models compliance risks under incomplete context and identifies unknown or ambiguous elements. Evaluated on benchmarks aligned with the GDPR and the EU AI Act, the approach significantly outperforms current methods, not only improving judgment accuracy but also effectively surfacing contextual uncertainties inherent in compliance assessments.

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: AI for Accessibility

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Security and privacy of machine learning and AI applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Individuals' concerns about data privacy and AI safety are highly contextualized and extend beyond sensitive patterns. Addressing these issues requires reasoning about the context to identify and mitigate potential risks. Though researchers have widely explored using large language models (LLMs) as evaluators for contextualized safety and privacy assessments, these efforts typically assume the availability of complete and clear context, whereas real-world contexts tend to be ambiguous and incomplete. In this paper, we propose ContextLens, a semi-rule-based framework that leverages LLMs to ground the input context in the legal domain and explicitly identify both known and unknown factors for legal compliance. Instead of directly assessing safety outcomes, our ContextLens instructs LLMs to answer a set of crafted questions that span over applicability, general principles and detailed provisions to assess compliance with pre-defined priorities and rules. We conduct extensive experiments on existing compliance benchmarks that cover the General Data Protection Regulation (GDPR) and the EU AI Act. The results suggest that our ContextLens can significantly improve LLMs' compliance assessment and surpass existing baselines without any training. Additionally, our ContextLens can further identify the ambiguous and missing factors.

Problem

• privacy
• AI safety
• legal compliance
• context ambiguity
• LLM evaluation

Innovation

• ContextLens
• legal compliance
• large language models
• contextual ambiguity
• privacy and safety assessment