AI Safety Scoring Model for Consistent Product Certification
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Solution Overview
Problem
Current assessment techniques for AI-based products are inadequate in comprehensively addressing multifaceted risks, lacking standardized procedures for data handling, algorithm performance, and ethical considerations, and are not adaptable to the dynamic nature of AI technologies and evolving safety standards.
Innovation Solution
A computer-implemented method using machine learning to evaluate AI-based products against a set of AI safety principles, determining relevancy, conformance, and relative weights of requirements, with a configurable threshold for certification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning assessment is implemented, then productivity and consistency of safety evaluation is improved, but device complexity and implementation difficulty increases
Solution Approach 1:
The patent introduces an automated machine learning model as an intermediary between safety evaluators and AI-based products. This model serves as a mediator that objectively assesses conformance to safety principles, reducing human error and variability while maintaining high productivity in safety evaluations.
Solution Approach 2:
The patent replaces manual, human-based safety assessment mechanisms with automated machine learning systems. This substitution eliminates the need for subjective human judgment in evaluating conformance to safety principles, thereby improving consistency and productivity while reducing human error and variability.
2Reliability
If comprehensive safety principles are applied, then reliability and thoroughness of assessment is improved, but assessment time and complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining comprehensive safety principles and their associated requirements before the actual assessment process. The machine learning model is trained in advance on these principles, enabling it to rapidly evaluate AI-based products against all safety criteria simultaneously, thereby maintaining high reliability without excessive assessment time.
Solution Approach 2:
The automated machine learning assessment enables continuous evaluation of multiple safety principles in parallel. Rather than sequentially assessing each principle, the system continuously processes all safety requirements simultaneously, maintaining high reliability while significantly reducing total assessment time.
3Adaptability or versatility
If manual assessment processes are used, then flexibility in handling diverse AI products is maintained, but measurement precision and consistency deteriorates
Solution Approach 1:
The patent creates a universal machine learning assessment system that can evaluate diverse AI-based products against multiple safety principles simultaneously. This multi-functional system maintains adaptability to handle various product types while ensuring consistent and precise measurement of conformance to safety requirements, eliminating the variability inherent in manual processes.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the machine learning model's evaluation criteria and thresholds based on the specific characteristics of different AI-based products. This allows the system to maintain flexibility and adaptability across diverse products while preserving measurement precision and consistency through standardized evaluation parameters.
Data Source
AI summary
Systems and methods for assessing AI safety scores for AI-based products and facilitating the certification of these products based on their adherence to established AI safety principles, are provided. A trained machine learning model determines the relevancy of specific safety requirements to a given AI-based product. A degree of conformance of the AI-based product to these relevant requirements is determined and relative weights to each requirement are assigned. The AI safety score is calculated based on these parameters, with the AI-based product achieving AI safety certification if the score meets or exceeds a configurable safety threshold.