Prioritized AI Ethic Checklist Generation for Risk Analysis
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Solution Overview
Problem
Existing AI system ethical risk assessment methods are cumbersome for developers and providers, requiring significant effort to adapt risk components into the AI system and stakeholders, and fail to efficiently recognize ethical risks when the AI system configuration changes.
Innovation Solution
A machine learning program that generates a prioritized AI ethic checklist by analyzing the relationships between AI system components and stakeholders, visualizing ethical risks, and automatically selecting significant check items based on a graph structure, reducing the load of ethical risk analysis and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a comprehensive checklist is applied to analyze ethical risks in AI systems, then the coverage of ethical risk detection is improved, but the complexity and time required for analysis increases
Solution Approach 1:
The patent segments the comprehensive ethical risk checklist into multiple priority levels (first priority, second priority, third priority). This segmentation allows analysts to focus on high-priority items first while maintaining the option to review lower-priority items if needed, thereby reducing the immediate complexity burden while preserving comprehensive coverage capability.
Solution Approach 2:
The patent extracts and separates high-priority check items from the comprehensive checklist based on predetermined criteria. By taking out the most critical ethical risk indicators and presenting them separately, the system reduces the initial analysis complexity while ensuring that the most important risks are detected with high reliability.
2Reliability
If a comprehensive checklist is applied to analyze ethical risks in AI systems, then the coverage of ethical risk detection is improved, but the time required for analysis increases
Solution Approach 1:
The patent segments the comprehensive ethical risk checklist into multiple priority levels. This allows analysts to complete the high-priority portion of the analysis quickly while maintaining the option to proceed to lower-priority items if time permits or risks are identified, thereby reducing the minimum analysis time while preserving comprehensive coverage capability.
Solution Approach 2:
The patent enables partial action by allowing analysts to focus on first-priority check items when time is constrained, while still providing the framework for complete analysis when time is available. This partial action approach ensures that the most critical ethical risks are always assessed, providing reliable detection coverage adapted to available time resources.
3Measurement precision
If risk components are adapted into the AI system and stakeholders manually, then the accuracy of ethical risk assessment is improved, but the effort and complexity required increases
Solution Approach 1:
The patent establishes predetermined relationships between check items and risk components before the actual ethical risk assessment is performed. This preliminary action includes pre-defining the associations between checklist items, risk components, and stakeholders, which eliminates the need for manual adaptation during the assessment process while maintaining assessment accuracy.
Solution Approach 2:
The patent uses pre-established templates and predetermined relationships that can be copied and applied to different AI systems. Instead of manually adapting risk components for each new system, the predetermined structures serve as reusable templates that maintain accuracy while significantly reducing the effort required for each new assessment.
Data Source
AI summary
A computer-readable recording medium has stored therein a machine learning program executable by one or more computers, the machine learning program including: an instruction for comparing a first plurality of relationship information pieces with a second plurality of relationship information pieces, the first plurality of relationship information pieces being determined in terms of an inputted configuration of a first Artificial Intelligence (AI) system and each including a plurality of attributes, the second plurality of relationship information pieces being determined in terms of a second AI system; an instruction for determining priorities of the first plurality of relationship information pieces, the priorities being based on a result of the comparing; and an instruction for outputting, as a checklist of the first AI system, one or more check items selected in accordance with the determined priorities from among a plurality of check items associated with the plurality of attributes.