CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data (2409.13903v1)

Abstract: Advances in generative AI point towards a new era of personalized applications that perform diverse tasks on behalf of users. While general AI assistants have yet to fully emerge, their potential to share personal data raises significant privacy challenges. This paper introduces CI-Bench, a comprehensive synthetic benchmark for evaluating the ability of AI assistants to protect personal information during model inference. Leveraging the Contextual Integrity framework, our benchmark enables systematic assessment of information flow across important context dimensions, including roles, information types, and transmission principles. We present a novel, scalable, multi-step synthetic data pipeline for generating natural communications, including dialogues and emails. Unlike previous work with smaller, narrowly focused evaluations, we present a novel, scalable, multi-step data pipeline that synthetically generates natural communications, including dialogues and emails, which we use to generate 44 thousand test samples across eight domains. Additionally, we formulate and evaluate a naive AI assistant to demonstrate the need for further study and careful training towards personal assistant tasks. We envision CI-Bench as a valuable tool for guiding future LLM development, deployment, system design, and dataset construction, ultimately contributing to the development of AI assistants that align with users' privacy expectations.

• The paper presents CI-Bench, a benchmark that rigorously evaluates AI assistants' contextual privacy handling using synthetic data across diverse domains.
• It features a multi-step data generation pipeline with over 44,000 test cases assessing critical aspects like context understanding and norm identification.
• Experimental results reveal strong context comprehension in models but expose challenges in nuanced appropriateness judgments under privacy norms.

An Analytical Overview of CI-Bench: Benchmarking Contextual Integrity of AI Assistants

The paper, "CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data," introduces a comprehensive benchmark designed to scrutinize the privacy management capabilities of AI assistants through the application of the Contextual Integrity (CI) framework. This paper is particularly timely given the increasing reliance on AI-driven systems capable of data personalization, highlighting an urgent need for frameworks that assess how these systems manage sensitive user information. The use of synthetic data for this benchmark underscores the scalability and adaptability necessary to evaluate AI systems across varied contexts.

Key Contributions

Benchmark Design and Data Generation

The authors have constructed CI-Bench as a synthetic benchmark to rigorously evaluate AI assistants' ability to manage personal data flows. CI-Bench is built around a multi-step synthetic data generation pipeline that fabricates over 44,000 test cases across eight domains, including eCommerce, Healthcare, and Education, using structured and unstructured conversational data formats such as dialogues and emails. The framework dissects information flow mediation into essential components: context understanding, norm identification, appropriateness judgment, and response generation. This decomposition allows thorough evaluation of AI assistants across tasks that simulate real-world complexities of information exchange.

Evaluation of Information Flow

The benchmark predominantly applies the CI framework, focusing on the contextuality of information flow by considering dimensions such as actors, information types, and transmission principles. Notably, CI-Bench includes MCQ tasks to test context comprehension and norm identification. The assessment also involves evaluating appropriateness judgment, using human-annotated test sets to determine whether the AI assistant can understand and enforce privacy expectations effectively.

Experimental Findings

The experiment results reveal that state-of-the-art models exhibit significant proficiency in understanding contextual elements and applying these to normative frameworks. However, they face challenges in appropriateness judgment, indicating room for enhancement in nuanced scenarios where societal norms about information sharing become more complex. The sensitivity of model performance to factors like model size and explicit norm presentation validates the necessity for scalable benchmarks like CI-Bench for holistic evaluation. Specifically, the experimental data suggest that improvements can be achieved through better guidelines and larger, more contextually aware models.

Implications and Future Directions

CI-Bench has dual implications. Practically, it serves as a tool to inform the training and deployment of AI assistants, aiming to align these systems with privacy expectations. Theoretically, it challenges existing paradigms of privacy evaluation, encouraging richer explorations into the intersections of AI and human societal factors such as norms and trust.

Despite its comprehensive nature, CI-Bench does have limitations. The labeling bias, due to potential inaccuracies in human annotations, and the absence of cultural nuance across different societal norms are areas requiring refinement. Future iterations could incorporate a broader spectrum of rater pools and explore multimodal data, ensuring that AI systems are evaluated comprehensively with respect to real-world biases and complexities. Moreover, enhancing models to articulate their decision-making processes—or justifications—would add a layer of transparency crucial for aligning with human ethical standards.

Conclusively, CI-Bench is a substantial step toward developing privacy-conscientious AI, with a promise of steering future models toward considerations that balance utility with user trust and confidentiality. The proposition of CI-Bench underscores the necessity for ongoing research to develop AI systems that not only understand context but also navigate the nuanced terrain of human normative frameworks.