Validating ML Explainability via Synthetic Data Optimization
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Solution Overview
Problem
Existing attribution-based explainability methods for machine learning systems lack reliable validation, leading to uncertain trustworthiness of their results due to limited validation on specific data sets.
Innovation Solution
A method using generative models to optimize synthetic data points to cause attribution-based explainability methods to fail, allowing for the identification of unreliable outputs by assessing the distribution of explainability scores and data association, thereby validating the method's reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If attribution-based explainability methods are used to validate machine learning systems, then explainability results can be obtained, but the validation reliability is limited due to dependence on specific data sets
Solution Approach 1:
The patent creates synthetic copies of data points by generating perturbed versions of original training data. These synthetic data points replicate the structure and characteristics of real data while allowing controlled manipulation. By validating explainability methods on these copied synthetic data points, the system achieves more reliable validation that is not limited to specific original data sets.
Solution Approach 2:
The patent systematically changes parameters of synthetic data points by applying different noise vectors and perturbation magnitudes. This creates a distribution of synthetic data points with varying characteristics, allowing the validation process to test explainability methods across multiple parameter configurations rather than being constrained to a single data set.
2Productivity
If validation is performed on a limited set of validation data, then validation can be completed, but the explainability results cannot be fully trusted
Solution Approach 1:
The patent performs preliminary generation of synthetic validation data points before executing the explainability validation. By pre-generating a comprehensive distribution of synthetic data points with various perturbations, the system prepares an extensive validation corpus in advance, enabling thorough validation without requiring extensive computational resources during the actual validation process.
Solution Approach 2:
The system uses the machine learning model's own training data to generate synthetic validation data points through perturbation. This self-service approach eliminates the need for separate external validation data sets, allowing the model to validate its own explainability methods using derived versions of its training data.
3Reliability
If synthetic data points are generated with perturbations to validate explainability methods, then more comprehensive validation is achieved, but the complexity of the validation process increases
Solution Approach 1:
The patent divides the validation process into distinct segments: generating base synthetic data points, applying noise vectors, computing explainability metrics, and evaluating results. This segmentation allows each component to be independently optimized and managed, reducing overall process complexity while maintaining comprehensive validation coverage.
Data Source
AI summary
A method for validating an attribution-based explainability method for a machine learning system. The method includes ascertaining synthetic data points using a generator according to a noise vector; ascertaining an output of the machine learning system by propagating the synthetic data point through the machine learning system and ascertaining an explanation output using the attribution-based explainability method for the ascertained output; ascertaining a score of the explanation output; optimizing the noise vector with regard to the score so that the score moves to the rear part of the distribution of scores; ascertaining further synthetic data points using a generator according to the optimized noise vector; ascertaining a further output of the machine learning system by propagating the further synthetic data points through the machine learning system and ascertaining a further explanation output using the attribution-based explainability method for the further ascertained output; and validating the attribution-based explainability method.