AI Model Interpretability via Risk Component Detection
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Solution Overview
Problem
Conventional decision-making support models using reinforcement learning or mathematical optimization lack interpretability, failing to provide insights into their decision-making processes and whether better alternatives exist.
Innovation Solution
A computer system that includes an arithmetic device and a storage device, configured to explain the prediction processing of a model by determining the contributions of components to an action value and its uncertainty, detecting risk components, and presenting this information to support human decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a planning model utilizing reinforcement learning or mathematical optimization is applied to supporting human decision-making, then the productivity and efficiency of decision-making are improved, but the interpretability of the model's recommendations deteriorates
Solution Approach 1:
The patent segments the model's decision-making process into analyzable components by identifying and explaining the contributions of individual input features to the action value. This allows the complex black-box model to be broken down into interpretable feature-level explanations, resolving the contradiction between maintaining model efficiency and providing interpretability.
Solution Approach 2:
The patent introduces an explanation generation mechanism as an intermediary between the planning model and the user. This intermediary translates the model's internal computations into human-understandable explanations about which features contributed most to the recommended action, preserving both the model's computational efficiency and the user's ability to understand the reasoning.
2Device complexity
If conventional models are used without explanation mechanisms, then the device complexity is reduced, but the ability to provide actionable insights deteriorates
Solution Approach 1:
The patent enables the model to self-explain its own decisions by automatically generating explanations that highlight the most influential features. Instead of requiring external analysis tools or complex post-processing, the model serves its own interpretability needs through integrated explanation generation, maintaining simplicity while providing actionable insights.
Data Source
AI summary
A computer system includes an arithmetic device and a storage device. The storage device stores a model configured to output an action predicted based on an action value in response to input data. The arithmetic device is configured to acquire data to be explained including values of a plurality of components to be explained in order to explain first prediction processing of the model that outputs a first predicted action in response to first input data, determine contributions of each of the plurality of components to be explained to an action value and an uncertainty of the action value in the first prediction processing, detect one or more risk components in the first prediction processing from the plurality of components to be explained based on the contributions, and present information on the risk components.