Prediction Rationale Analysis Apparatus for Model Discrepancy Detection
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Solution Overview
Problem
Current techniques for analyzing prediction rationales in machine learning models do not effectively compare differences between prediction models or between human and model predictions, making it difficult to determine the cause of discrepancies in prediction results.
Innovation Solution
A prediction rationale analysis apparatus and method that stores multiple prediction models, executes predictions on input data, identifies sets of models producing similar results, and compares model properties to infer common and differing rationales between prediction models and external predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple prediction models are analyzed individually using existing techniques, then prediction rationale for each model can be extracted, but comparison of differences between models or between human and model predictions cannot be performed
Solution Approach 1:
The patent combines multiple individual model predictions into a unified analysis framework. By aggregating prediction results from multiple models and human predictors, the system creates a comprehensive prediction rationale that enables comparison across different prediction sources while maintaining the analytical precision of individual model evaluation.
Solution Approach 2:
The patent develops a universal prediction rationale analysis apparatus that can handle multiple types of predictors (different machine learning models and human predictions) through a single integrated system. This multi-functional approach allows the same analytical framework to extract and compare rationales across diverse prediction sources, enhancing both precision and versatility.
2Ease of operation
If prediction rationales are extracted for individual models, then interpretability of single model predictions is improved, but ability to determine cause of discrepancies between different predictions is lost
Solution Approach 1:
The patent merges individual model prediction rationales with human prediction rationales into a unified comparative analysis. This combination preserves the interpretability benefits of individual rationale extraction while adding comparative context that reveals the causes of discrepancies between different prediction sources.
Solution Approach 2:
The system implements feedback by comparing prediction rationales across multiple models and human predictors, then using these comparisons to refine and enhance the interpretability of individual predictions. The comparative analysis provides feedback information that helps identify why different predictors arrive at different conclusions, preserving both individual interpretability and comparative insight.
Data Source
AI summary
A prediction rationale analysis apparatus includes a prediction model that stores prediction models designed or trained to solve common problems, a prediction execution part that makes a prediction on the prediction target based on each prediction model to derive a model prediction result, a prediction rationale analyzer that identifies a set of prediction models based on which the model prediction result identical or approximate to the external prediction result is derived from the prediction target, and infers, as a prediction rationale for the external prediction result, a property common to the prediction models belonging to the set, and a prediction rationale difference analyzer that compares a property of any prediction model with the prediction rationale for the external prediction result to derive a prediction rationale difference indicating a difference in prediction rationale between the prediction model and the external prediction, and outputs information based on the prediction rationale difference.