AI Safety Verification Using Feature Combination Gap Analysis
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Solution Overview
Problem
Current systems lack effective methods for verifying the safety of artificial intelligence systems, particularly in ensuring the appropriateness and consistency of their internal logical structures, which is crucial for reliable operation.
Innovation Solution
A safety verification system that accepts feature quantity information from test data, judges combinations of values that the artificial intelligence system may not have encountered during training, and adds new test data to ensure completeness and consistency, using methods like FRAM and SpecTRM for analysis and verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive test data covering all possible feature quantity combinations is collected, then safety verification completeness is improved, but data collection time and cost increase significantly
Solution Approach 1:
The patent performs preliminary analysis of the AI system's internal logical structure before conducting safety verification tests. By预先 identifying incomplete or inconsistent logical combinations through static analysis of the neural network architecture and training data, the system can target specific test cases that need verification, rather than exhaustively testing all possible feature quantity combinations. This preliminary action significantly reduces the time and data required for comprehensive safety verification.
2Reliability
If the AI system's internal logical structure is made transparent and analyzable, then safety verification capability is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent introduces an intermediary analysis layer that sits between the AI system and the verification process. This intermediary component analyzes the internal logical structure of the neural network by examining feature quantity relationships, combination patterns, and logical consistency without requiring modification of the core AI system. The intermediary generates verification data and identifies logical inconsistencies, enabling safety verification while maintaining the original AI system's integrity and minimizing added complexity.
Data Source
AI summary
An effective system for verifying safety of an artificial intelligence system includes a feature quantity information accepting unit which accepts feature quantity information that includes values of plural feature quantities, that are assumed as those used in an artificial intelligence system, in each of plural first test data used for a test for verifying safety of the artificial intelligence system; and a judgment unit which judges a first combination, that is a combination that is not included in the plural first test data, in combinations of values that plural feature quantities may take, or a second combination, with it plural correct analysis results that should be derived by the artificial intelligence are associated, in the combinations of the values that the plural feature quantities may take.