arxiv_ai 95% Match Methodology/System Paper AI safety researchers,Developers of autonomous agents,AI ethicists 4 weeks ago

Contextual Integrity in LLMs via Reasoning and Reinforcement Learning

ai-safety › alignment

📄 Abstract

Abstract: As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI) -- what is the appropriate information to share while carrying out a certain task -- becomes a central question to the field. We posit that CI demands a form of reasoning where the agent needs to reason about the context in which it is operating. To test this, we first prompt LLMs to reason explicitly about CI when deciding what information to disclose. We then extend this approach by developing a reinforcement learning (RL) framework that further instills in models the reasoning necessary to achieve CI. Using a synthetic, automatically created, dataset of only $\sim700$ examples but with diverse contexts and information disclosure norms, we show that our method substantially reduces inappropriate information disclosure while maintaining task performance across multiple model sizes and families. Importantly, improvements transfer from this synthetic dataset to established CI benchmarks such as PrivacyLens that has human annotations and evaluates privacy leakage of AI assistants in actions and tool calls.

Key Contributions

Addresses contextual integrity (CI) for autonomous agents by developing a framework that instills reasoning about appropriate information disclosure. It combines explicit prompting with a reinforcement learning approach, significantly reducing inappropriate disclosures while maintaining task performance across various LLMs.

Business Value

Crucial for building trustworthy AI systems, especially those acting on behalf of users, by preventing privacy breaches and ensuring responsible information handling.

Paper Metadata

Innovation Type

Framework/Methodology

Deployment Feasibility

Medium, requires integration into agent decision-making pipelines and careful tuning.

Limitations Addressed

Ensuring autonomous agents share only appropriate information in context (contextual integrity) is a central challenge; existing methods may not instill the necessary reasoning.

Performance Gains

Substantially reduces inappropriate information disclosure,Maintains task performance

Technical Tags

contextual integrityautonomous agentsinformation disclosurereasoningreinforcement learningsynthetic datasettask performancemodel families

Research Topics

AI SafetyArtificial Intelligence EthicsMachine LearningReinforcement LearningLarge Language Models

Methods & Architectures

Prompting LLMs for explicit reasoningReinforcement learning frameworkSynthetic dataset generationEvaluation across model sizes/families Large Language Models (LLMs)Reinforcement Learning agents

Applications & Tasks

Autonomous agents AI decision-making Ensuring contextual integrityAppropriate information disclosure Decision making for autonomous agentsControlling information sharing

Datasets & Benchmarks

Datasets

Synthetic dataset (~700 examples)

Reduction in inappropriate information disclosureTask performance maintenance

Related Fields

AI EthicsPrivacy-Preserving AIAgent BehaviorExplainable AI (XAI)

Keywords

contextual integrityAI safetyautonomous agentsinformation disclosureLLM reasoningreinforcement learningAI ethicsprivacyresponsible AIdecision making

Academic Context

#AI Safety#Artificial Intelligence Ethics#Machine Learning#Reinforcement Learning#Large Language Models

Commercial Potential

Potential Products

Secure AI assistantsPrivacy-aware decision-making modules for AI agents

Target Industries

HealthcareFinanceCustomer serviceAny industry using AI agents

Use Case Examples

AI assistants that handle sensitive user dataAutonomous systems making decisions in regulated environmentsChatbots that need to maintain user privacy

Competitive Edge

Provides a principled approach to contextual integrity using reasoning and RL, going beyond simple rule-based systems or basic LLM prompting.

Market Opportunity

Growing market for trustworthy and safe AI solutions.

Revenue Models

Licensing of the safety moduleintegration services.

Resource Requirements

Compute Needs

Moderate to high, depending on the RL training and LLM inference.

Data Requirements

Requires a synthetic dataset designed for contextual integrity scenarios.

Deployment Constraints

Ensuring the learned reasoning generalizes well to unseen real-world contexts.

Scalability

The RL framework and LLM integration need to be scalable.

Regulatory Considerations

Data privacy regulations (e.g.GDPRCCPA) are highly relevant.

Production Readiness

Maturity Level

Research

Time to Market

2-3 years for robust integration into agent systems.

Patent Potential

Medium (novel RL framework for AI safety)

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