Ethical Risk Assessment for Explainable AI Models
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Solution Overview
Problem
White-box AI systems face challenges in assessing and mitigating ethical risks due to diverse model components and varying ethical frameworks across countries and linguistic areas, which can undermine corporate credibility if not properly managed.
Innovation Solution
A risk assessment apparatus and method that acquires explainable predictive models, determines ethical risks using ethical risk factor information, and selects models based on these assessments to output reliable and efficient models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive ethical risk assessment is performed on all possible models combining diverse components and variables, then assessment reliability is improved, but assessment complexity and time consumption increase significantly
Solution Approach 1:
The assessment framework is segmented into three distinct modules: model acquisition, risk determination, and model selection. Each module handles specific aspects of the assessment process independently, making the overall complex task manageable and systematic. The risk determination module further segments assessment into multiple evaluation dimensions (fairness, transparency, accountability, etc.), allowing comprehensive coverage without overwhelming complexity.
Solution Approach 2:
The system performs preliminary risk assessment actions before model deployment. By evaluating models against predefined ethical risk factors and criteria in advance, the system identifies and filters out high-risk models before they cause harm. This preliminary action approach allows comprehensive assessment of many models without requiring continuous monitoring of all possible model combinations.
2Adaptability or versatility
If ethical risk factors from multiple countries and linguistic areas are considered, then assessment comprehensiveness is improved, but the number of models to be assessed increases immensely
Solution Approach 1:
The risk determination module is designed with universal applicability across different cultural and linguistic contexts. It uses a standardized set of ethical risk factors that can be applied consistently across multiple countries and linguistic areas. The module processes diverse model components and variables through a unified assessment framework, maintaining comprehensive coverage while improving assessment throughput through standardization.
3Measurement precision
If detailed analysis of model components and explanatory variables is performed, then risk detection accuracy is improved, but processing time increases
Solution Approach 1:
The assessment system applies different levels of analysis to different model components based on their importance and risk characteristics. Critical components with higher potential for ethical risks receive more detailed analysis, while less critical components are assessed more superficially. This localized quality approach maintains high risk detection accuracy for important factors while reducing overall processing time.
Data Source
AI summary
There is provided a risk assessment apparatus having a model acquisition part that acquires at least one explainable predictive model; a risk determination part that determines risk in the at least one model on the basis of the at least one model and ethical risk factor information, which is information that is an ethical risk factor; a model selection part that selects a model on the basis of the result of risk determination; and a model output part that outputs the selected model.