Black-Box AI Audit Framework for Runtime Safety Assessment
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Solution Overview
Problem
Conventional verification and validation paradigms for AI systems assume design-time knowledge of system specifications and use-case requirements, which are invalidated by AI systems' ability to adapt to user-specific tasks and environments, making it impossible to assess their behavior accurately without access to internal designs.
Innovation Solution
A framework that includes an AI Interface Specification (AIIS) and an AI Assessment Tool (AIAT) to audit AI systems without requiring access to internal designs, using query generation methods to evaluate compliance with safety specifications and adapt to changes in user-specific tasks and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional verification and validation paradigms are used that assume design-time knowledge of system specifications, then assessment accuracy is improved when internal designs are accessible, but the approach becomes inapplicable to AI systems with adaptive behavior and user-specific deployments
Solution Approach 1:
The patent introduces an intermediary assessment framework that operates between the AI system and the auditor. This framework uses standardized interfaces and protocols to enable verification without requiring access to internal system designs, thus resolving the contradiction between maintaining assessment accuracy and adapting to black-box AI systems with user-specific deployments
Solution Approach 2:
The patent creates simplified copies or representations of AI system behavior through standardized interfaces and interaction protocols. These copies enable verification and validation activities without needing to access or understand the complex internal designs of adaptive AI systems, allowing assessment accuracy to be maintained while accommodating system adaptability
2Reliability
If design-time testing is performed to verify AI system behavior, then compliance with safety specifications is improved, but the ability to handle runtime variations in user tasks and environments deteriorates
Solution Approach 1:
The patent transitions from static design-time verification to dynamic runtime assessment mechanisms. The framework enables continuous verification of safety specifications during actual system operation, allowing the system to maintain compliance while adapting to varying user tasks and environments that cannot be predicted at design time
Solution Approach 2:
The patent establishes preliminary standardized interfaces and assessment protocols that are set up before deployment but enable flexible runtime verification. These preliminary structures allow the system to handle runtime variations while maintaining safety compliance through pre-established verification mechanisms
3Adaptability or versatility
If software updates are deployed to improve AI system functionality, then system capabilities are enhanced, but the inscrutability of changes to users and safety auditors increases
Solution Approach 1:
The patent implements feedback mechanisms through standardized interfaces that provide information about system changes to users and auditors. This feedback loop maintains transparency by communicating what changes have been made and their implications, even as the system evolves through software updates to enhance capabilities
4Reliability
If comprehensive testing of AI systems is conducted across millions of user-specific deployments, then coverage of use cases is improved, but the time and resources required for testing increase exponentially
Solution Approach 1:
The patent creates universal standardized interfaces and assessment protocols that can be applied across diverse user-specific deployments. This universal framework enables efficient testing by allowing the same verification mechanisms to work across millions of different use cases without requiring separate comprehensive testing for each deployment
Data Source
AI summary
Example systems and methods for AI capability assessment include query-based assessment of sequential decision making agents (SDMAs) in stochastic settings with minimal assumptions on SDMA internals. In these examples, a new approach is presented for modeling the capabilities of black-box AI systems by using an active learning approach that can effectively interact with the black-box AI systems and learn an interpretable probabilistic model describing the capabilities of the black-box AI systems.