GenAIPABench: A Benchmark for Generative AI-based Privacy Assistants (2309.05138v3)

Abstract: Privacy policies of websites are often lengthy and intricate. Privacy assistants assist in simplifying policies and making them more accessible and user friendly. The emergence of generative AI (genAI) offers new opportunities to build privacy assistants that can answer users questions about privacy policies. However, genAIs reliability is a concern due to its potential for producing inaccurate information. This study introduces GenAIPABench, a benchmark for evaluating Generative AI-based Privacy Assistants (GenAIPAs). GenAIPABench includes: 1) A set of questions about privacy policies and data protection regulations, with annotated answers for various organizations and regulations; 2) Metrics to assess the accuracy, relevance, and consistency of responses; and 3) A tool for generating prompts to introduce privacy documents and varied privacy questions to test system robustness. We evaluated three leading genAI systems ChatGPT-4, Bard, and Bing AI using GenAIPABench to gauge their effectiveness as GenAIPAs. Our results demonstrate significant promise in genAI capabilities in the privacy domain while also highlighting challenges in managing complex queries, ensuring consistency, and verifying source accuracy.

• The paper introduces GenAIPABench, a benchmark using diverse privacy queries to assess generative AI-based privacy assistants.
• It employs a comprehensive metric system measuring relevance, accuracy, clarity, and completeness across platforms like ChatGPT-4, Bard, and BingAI.
• Empirical results reveal performance variability and highlight challenges with paraphrased queries and complex privacy documents.

Generative AI-based Privacy Assistants: Benchmarks and Evaluations

This paper presents an important contribution to the domain of privacy management through generative AI by introducing a novel benchmark for evaluating Generative AI-based Privacy Assistants (GenAIPAs). Aamir Hamid and his colleagues have developed a framework that addresses the complex task of processing and answering privacy-related inquiries, thanks to the capabilities of LLMs.

Core Contributions

The benchmark, referred to as "GenAIPABench," offers a comprehensive approach to the evaluation of GenAIPAs which includes:

1. Question Corpus: A diverse set of questions related to various aspects of privacy policies and data protection regulations. These questions target areas like user control, transparency, security, and compliance, among others. The intent is to probe the AI's ability to provide precise and insightful information.
2. Evaluation Metrics: A sophisticated metric system addresses key performance dimensions, including relevance, accuracy, clarity, completeness, and reference. These metrics ensure a holistic evaluation of GenAIPA responses, crucial for user trust and the effectiveness of the tools.
3. Comprehensive Evaluator: An evaluator utility that not only poses questions to GenAIPAs but also manages the input presentation to these AI systems, testing the robustness of privacy responses under varied conditions.

Results and Observations

The paper presents empirical results obtained by deploying the benchmark against three prominent generative AI systems: ChatGPT-4, Bard, and BingAI. The authors provide detailed analyses of these systems across five distinct privacy policies and two major privacy regulations, GDPR and CCPA. Several observations emerge from this evaluation:

• High Variability in Performance: There was notable variability in performance across the systems and the different privacy policies. BingAI generally outperformed the others, indicating a baseline competence for handling structured questions about policies and regulations. However, all systems struggled with tightly referencing responses to specific sections of provided privacy documents.
• Challenges with Paraphrased Queries: Although explorative, the benchmark highlights consistent challenges these models face when questions are paraphrased. This suggests underlying limitations in the flexibility of the models to comprehend reworded queries, a critical area for real-world applicability where user queries naturally vary in form.
• Implication of Document Complexity: The complexity and length of privacy policies were highlighted as significant factors impacting GenAIPA performance. Shorter, more cohesive documents yielded better response accuracy and relevance, indicating that AI models still face challenges processing lengthy, complex texts effectively.

Implications and Future Directions

The work underscores the need for continued advancement in AI models to address the nuanced and legally stringent demands of privacy management. In particular, the findings suggest several future research pathways and practical implications:

• Domain-Specific Fine-Tuning: To improve the relevancy and completeness of responses, future models may require domain-specific adaptations, particularly concerning privacy laws and regulations.
• Improving Reference Accuracy: Enhanced training strategies that foster precise document referencing could improve trust and reliability, essential for user acceptance of GenAIPAs.
• Strategic Expansion of Benchmarks: Future iterations of this benchmark could integrate multiple language representations and more diversified privacy documents, enhancing the robustness and readiness of GenAIPAs.

Concluding Remarks

This paper is instrumental in guiding future developments in the application of generative AI to privacy management. By providing a rigorous evaluation framework, the work lays the foundation for building more sophisticated privacy assistants that effectively interpret and communicate complex privacy documents. As researchers continue to refine these AI models and benchmarks, it is ultimately the end-users who will benefit from enhanced privacy clarity and protection across digital landscapes.