Machine Trust Index for AI Reliability Assessment
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Solution Overview
Problem
The growth and adoption of artificial intelligence (AI) and machine learning (ML) are hindered by challenges related to social license, including concerns over privacy, security, reliability, transparency, and ethical issues, exacerbated by the easy availability of data, algorithms, and code without adequate security checks, leading to questionable accuracy and trustworthiness of AI systems.
Innovation Solution
A method and system for determining a machine trust index (MTI) that evaluates AI processes using a configurable and auditable set of criteria, including statistical measures, absence of bias, and trustworthiness of training data, employing principal component analysis (PCA) to generate a quantified measurement of trustworthiness, which can be used to compare different algorithms and models, and suggests enhancements for improving trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI systems are made more accessible and widely adopted, then productivity and socioeconomic benefits increase, but reliability and trustworthiness deteriorate due to security concerns and questionable accuracy
Solution Approach 1:
The patent introduces an intermediary evaluation system that acts as a mediator between AI systems and users. This system includes automated evaluation tools, certification mechanisms, and transparency reports that verify and communicate the reliability of AI systems, thereby enabling wider adoption without sacrificing trustworthiness
Solution Approach 2:
The patent implements feedback mechanisms through continuous monitoring and evaluation of AI system performance. The system collects data on AI behavior, compares it against established criteria, and provides feedback loops that improve reliability assessment over time, allowing productivity to grow while maintaining trust through demonstrated performance
2Ease of manufacture
If developers use readily available code and algorithms from online repositories, then ease of manufacture and accessibility improve, but security and accuracy deteriorate due to lack of security checks
Solution Approach 1:
The patent applies preliminary action by conducting security evaluations and accuracy assessments before AI systems are deployed or updated. The evaluation system pre-approves code and algorithms from repositories, performing security checks in advance so that developers can safely use available resources without compromising security
Solution Approach 2:
The patent introduces an intermediary evaluation layer between developers and code repositories. This intermediary system automatically scans, evaluates, and certifies code before it can be used, maintaining accessibility while filtering out insecure or inaccurate code through automated security protocols
3Ease of operation
If end-users install AI systems without adequate knowledge of code provenance and inner functioning, then ease of operation improves, but reliability deteriorates due to inability to assess potential risks
Solution Approach 1:
The patent introduces an intermediary trust certification system that stands between end-users and AI systems. This intermediary provides automated trust indicators, provenance information, and risk assessments that users can rely on without needing to understand the underlying code, thereby maintaining ease of operation while improving reliability through expert-mediated verification
Solution Approach 2:
The patent replaces the mechanical need for user expertise in code analysis with an automated electronic evaluation system. Instead of requiring users to manually assess code quality and security, the system uses automated tools to generate trust indicators and risk assessments, substituting human expert analysis with scalable automated evaluation
Data Source
AI summary
The present disclosure provides a system and method for use in evaluating the trustworthiness of an artificial intelligence (AI) process. The trustworthiness evaluation may include both automated and manual evaluation of the AI process. Further, the system is provided with functionality for automatically evaluating and modifying the criteria used in evaluating the AI process.