AI Ethics Checklist Generation via Graph Structure Analysis
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Solution Overview
Problem
Current AI system checklists and risk chain models are difficult to implement, leading to inaccuracies and inefficiencies in identifying ethical risks due to the complexity of stakeholder relationships and AI system components, resulting in either omitted or excessive check items.
Innovation Solution
A computer-readable medium storing an AI system check program that specifies stakeholder sets, compares them to a template, and outputs differences to refine the system diagram, automatically generating an AI ethics checklist prioritized by graph structure characteristics to improve risk analysis accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of stakeholder relationships and AI system components is performed using checklists and risk chain models, then comprehensive ethical risk assessment can be achieved, but the process becomes highly time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-based system that uses graph structure analysis and machine learning algorithms to identify ethical risks, thereby eliminating human error and significantly reducing analysis time while maintaining comprehensive assessment capability
Solution Approach 2:
The system creates a graph-based representation (copy) of the complex stakeholder relationships and AI system components, allowing automated analysis of ethical risks without requiring manual examination of each relationship, thus reducing time loss while preserving assessment accuracy
2Reliability
If comprehensive checklists covering all potential ethical risks are created, then thorough risk assessment is achieved, but the number of check items becomes excessive and difficult to manage
Solution Approach 1:
The system extracts only the relevant ethical risk check items that are actually applicable to the specific AI system being analyzed, based on its graph structure characteristics, rather than presenting all possible check items. This reduces complexity while maintaining complete coverage of applicable risks
Solution Approach 2:
The patent applies different levels of scrutiny and generates different numbers of check items based on the local characteristics of each AI system's graph structure, rather than using a uniform comprehensive checklist for all systems. This optimizes the balance between completeness and manageability
3Measurement precision
If detailed analysis of all stakeholder relationships is performed to ensure accuracy, then ethical risk identification improves, but the complexity of the analysis process increases
Solution Approach 1:
The patent replaces complex manual analysis processes with automated computer-based graph structure analysis that precisely identifies stakeholder relationships and ethical risks without requiring manual traversal of all relationships, thereby maintaining high precision while reducing process complexity
Solution Approach 2:
The system segments the complex analysis task into distinct automated steps: graph construction from system documentation, graph structure analysis to identify key relationships, and automated generation of ethical risk check items. This segmentation maintains measurement precision while simplifying the overall process complexity
Data Source
AI summary
A non-transitory computer-readable recording medium storing an artificial intelligence (AI) system check program for causing a computer to execute processing including: specifying a first plurality of sets of stakeholders that have relationships with each other, based on a configuration of the stakeholders of an AI system; comparing the first plurality of sets with a second plurality of sets of stakeholders determined based on the configuration of the stakeholders of another AI system; and outputting a first set included in the first plurality of sets but not included in the second plurality of sets, as difference information, based on a result of the comparing.