AI Model Evaluation Using RAI Health Scoring
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Solution Overview
Problem
Conventional AI systems rely heavily on manual user intervention for evaluating the safety, trustworthiness, and truthfulness of AI models, which is time-consuming, prone to errors, and lacks comprehensive coverage of ethical standards, especially for advanced models like GPT-4, and does not account for regulatory frameworks such as the EU AI Act or NIST AI risk management frameworks.
Innovation Solution
A system and method for automatically evaluating AI models using algorithmic metrics, including RAI score and health score calculations, to determine their responsible AI status, incorporating ethical standards and regulatory compliance, and enabling dynamic updates based on feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual user intervention is used to evaluate AI models, then comprehensive assessment of safety and trustworthiness can be achieved, but the process becomes time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual human evaluation (mechanical system) with an automated computer-based evaluation system that uses algorithms and metrics to assess AI models. The system automatically calculates responsible AI scores, health scores, and compliance metrics without requiring human intervention, thereby eliminating time consumption and human error while maintaining comprehensive assessment coverage.
Solution Approach 2:
The evaluation system performs self-assessment of AI models by automatically executing evaluation algorithms, calculating metrics, and generating compliance reports. The system serves itself by autonomously evaluating multiple AI models against predefined ethical standards and regulatory requirements without external human input, achieving both speed and reliability.
2Adaptability or versatility
If manual evaluation methods are used, then detailed assessment can be performed, but the process lacks comprehensive coverage of ethical standards and regulatory frameworks
Solution Approach 1:
The patent creates a universal evaluation system that can assess multiple types of AI models (predictive, generative, regression-based) against a comprehensive set of ethical standards and regulatory frameworks (EU AI Act, NIST frameworks). The system performs multiple functions including calculating responsible AI scores, health scores, compliance metrics, and risk assessments within a single integrated platform, achieving broad coverage without proportionally increasing complexity.
3Productivity
If automated evaluation systems are implemented, then efficiency and speed improve, but the system requires complex algorithmic metrics and calculations
Solution Approach 1:
The patent segments the evaluation process into distinct modular components: data collection module, metric calculation module, scoring module, and reporting module. Each module handles specific algorithmic calculations (R-squared score, MAPE, MAE, D-squared score, explained variance score, LIME score, Cramér-von Mises statistic, Regressor Uncertainty score), allowing the complex evaluation system to process multiple metrics efficiently through organized, manageable segments rather than monolithic complexity.
Data Source
AI summary
A method for automatic evaluation of an artificial intelligence (AI) model is presented. The method can include receiving, at a computer system comprising a processor and a memory storing instructions executable by the processor, a pretrained AI model, an input dataset, and an expected predictions dataset, wherein the expected predictions dataset comprises expected results of the pretrained AI model based on the input dataset. The method can include generating an actual predictions dataset using the pretrained AI model by providing, by the processor, the input dataset as input to the pretrained AI model and receiving, at the computer system, as output from the pretrained AI model a plurality of predictions based on the input dataset. The method can include calculating, by the processor, a plurality of algorithmic metrics based on the expected predictions dataset and the actual predictions dataset. The method can include determining, by the processor, a responsible AI (RAI) score based on the plurality of algorithmic metrics. The method can include calculating, by the processor, a RAI health score based on the RAI score and a safety fraction. The method can include comparing, by the processor, the RAI health score to a RAI health metric. The method can include determining, by the processor, an overall RAI health of the pretrained AI model based on the comparison.