Counterfactual Background Generator for Black Box Model Interpretability
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Solution Overview
Problem
Existing methods for explaining decisions made by predictive models, such as SHAP, face challenges in selecting a relevant background dataset, leading to limited interpretability and accessibility, especially when training data is proprietary or unavailable.
Innovation Solution
A counterfactual explainer algorithm generates background data points aligned with the domain of the predictive model, allowing for intuitive and customizable explanations using a reference point, such as a zero value or average house, to determine feature contributions without requiring access to internal model details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional explanation methods like SHAP are used with existing background datasets, then model decisions can be explained, but the explanations lack intuitiveness and accessibility when training data is proprietary or unavailable
Solution Approach 1:
The patent creates synthetic background data points that copy the structural and statistical properties of the actual training data distribution without requiring access to the proprietary training data itself. These synthetic copies serve as substitutes for the unavailable training data, enabling explanation generation while preserving the necessary statistical characteristics for meaningful interpretations
Solution Approach 2:
The system transforms the explanation generation process by changing from using actual training data parameters to using synthetically generated parameters that match the desired data distribution. This allows the explanations to be grounded in statistically valid reference points even when the original training data is inaccessible
2Measurement precision
If proprietary training data is required for generating background datasets, then explanations can be more accurate, but the system becomes less accessible and harder to deploy
Solution Approach 1:
The patent extracts only the essential statistical properties and data distribution characteristics needed for generating meaningful explanations, separating these from the proprietary training data itself. This extraction allows the creation of synthetic background data that captures the necessary statistical structure without requiring access to the actual proprietary data, thereby enabling broad deployability while maintaining explanation quality
Solution Approach 2:
The synthetic data generation system is designed to be universally applicable across different predictive models and data types. By creating a flexible framework that can generate domain-appropriate synthetic background data without model-specific proprietary information, the system achieves both accuracy and broad adaptability for explaining various black-box models
3Productivity
If arbitrary background datasets are used, then explanations can be generated, but the quality and intuitiveness of the explanations deteriorate
Solution Approach 1:
The system performs preliminary configuration by allowing users to specify domain-relevant reference points and data distribution characteristics before generating explanations. This preliminary setup ensures that the synthetic background data is pre-aligned with domain expectations and statistical requirements, enabling both fast generation and high-quality, intuitive explanations without requiring iterative adjustments
Data Source
AI summary
A plurality of perturbed seed data values may be generated by performing a plurality of perturbation operations on an initial value to be processed by a predictive model. A plurality of counterfactual operations may be performed to generate a plurality of background data values of a background data store based on respective ones of the plurality of perturbed seed data values, a reference value within a domain of a predictive model, and the predictive model. A model analysis engine may be executed to generate a model analysis of the predictive model utilizing the background data store and the initial value.