Surrogate Rule Selection for Black-Box Model Interpretability
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Solution Overview
Problem
Existing model-agnostic explanation techniques for black-box models are unreliable as they may produce explanations that differ significantly from the original model's predictions, leading to reduced accuracy and human acceptance issues.
Innovation Solution
An information processing device and method that selects a surrogate rule from a pre-defined rule set by minimizing the error between the black-box model's predictions and the rule's predictions, ensuring the surrogate rule closely matches the original model's output, thereby increasing reliability and interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a black-box model with complex internal structure is used to achieve high prediction performance, then prediction accuracy is improved, but interpretability deteriorates making it difficult for humans to understand the reason of the prediction
Solution Approach 1:
The patent introduces a decision tree as an intermediary model that mediates between the black-box model's high accuracy and human interpretability needs. The decision tree is trained to replicate the black-box model's predictions while maintaining a transparent, rule-based structure that humans can understand and trace, thus serving as a bridge between accuracy and interpretability.
Solution Approach 2:
The patent creates a simplified copy of the black-box model's behavior using a decision tree. Instead of trying to understand the complex internal structure of the black-box model, the method trains a new model (decision tree) to copy the input-output behavior of the original model, providing an interpretable representation that mimics the accurate predictions without inheriting the complexity.
2Reliability
If explanation methods depend on the internal structure of particular black-box models, then model-specific optimization is improved, but adaptability deteriorates making it inapplicable to other models
Solution Approach 1:
The patent employs a universal explanation approach using decision trees that can be applied to any black-box model regardless of its internal structure. The method treats all black-box models uniformly by training a decision tree to replicate their predictions, making the explanation technique model-agnostic and broadly applicable across different model types while maintaining reliability.
3Device complexity
If a highly interpretable model is trained only on examples in the vicinity of inputted examples, then local interpretability is improved, but prediction accuracy deteriorates causing significant differences from the original model's predictions
Solution Approach 1:
The patent uses feedback from the black-box model's predictions to guide the training of the decision tree. By minimizing the difference between the decision tree's predictions and the black-box model's predictions across the training data, the method ensures that the simplified model maintains high accuracy while preserving interpretability, creating a feedback loop that aligns the two models.
Data Source
AI summary
In an information processing device, an observation data input means receives a pair of observation data and a predicted value of a target model for the observation data. A rule set input means receive a rule set including a plurality of rules, the rule including a pair of a condition and a predicted value corresponding to the condition. A satisfying rule selection means selects a satisfying rule from the rule set, the satisfying rule being a rule in which the condition becomes true for the observation data. An error calculation means calculates an error between a predicted value of the satisfying rule for the observation data and the predicted value of the target model. A surrogate rule determination means associates the rule which minimizes the error, among the satisfying rules, with the observation data as a surrogate rule for the target model.