Interpretation Gaps in LLM-Assisted Comprehension of Privacy Documents

2025-03-15

AI Summary

This work identifies four critical explanation gaps—accuracy loss, incompleteness, lack of clarity, and representational bias—that arise when large language models (LLMs) simplify privacy policies. Using a novel multidimensional human evaluation framework, augmented by prompt engineering optimization and cross-model consistency checking, the study systematically identifies and categorizes recurrent errors made by mainstream LLMs (e.g., GPT-4, Claude, Llama) in key tasks: clause extraction, semantic reduction, and responsible-party identification—including omissions of core obligations, semantic distortion, and misattribution of accountability. The work introduces the first evaluation framework specifically designed to assess LLM interpretability for trustworthy privacy assistance. It establishes an empirical benchmark and delivers actionable, model-agnostic improvement guidelines. By bridging gaps between technical capability and regulatory expectations, this research advances the robustness, transparency, and compliance readiness of privacy-focused AI systems. (149 words)

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: Ethics — Bias, Fairness, Transparency & PrivacyHumans and AI: AI for Accessibility

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved informationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This article explores the gaps that can manifest when using a large language model (LLM) to obtain simplified interpretations of data practices from a complex privacy policy. We exemplify these gaps to showcase issues in accuracy, completeness, clarity and representation, while advocating for continued research to realize an LLM's true potential in revolutionizing privacy management through personal assistants and automated compliance checking.

Problem

• Gaps in LLM interpretation of privacy policies
• Issues in accuracy, completeness, and clarity
• Need for research to enhance LLM privacy management

Innovation

• LLM simplifies complex privacy policies
• Identifies accuracy and clarity gaps
• Advances automated compliance checking