Black Box Model Prediction Characterization via Input Perturbation
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Solution Overview
Problem
In the context of data processing and machine learning, black box models pose a challenge as users cannot discern the relative influence of features on predictions, making it difficult to understand or improve model performance without access to proprietary algorithms.
Innovation Solution
A method is implemented to programmatically characterize black box models by generating perturbed input data points, analyzing their predictions, and determining the most influential features, thereby providing a human-readable explanation of the prediction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If proprietary black box algorithms are used for predictions, then prediction accuracy and vendor expertise are improved, but user understanding and feature influence discernment deteriorate
Solution Approach 1:
The patent introduces an intermediary characterization system that sits between the black box model and the user. This system generates synthetic data points, perturbs input features, and analyzes prediction changes to create an explanatory layer that reveals feature influences without exposing the proprietary algorithm itself.
Solution Approach 2:
The patent creates copies of the black box model's behavior through synthetic data generation. By generating synthetic input data points that mirror the structure and relationships of real data, the system can probe the model's responses and reconstruct feature importance information without accessing the actual model internals.
2Ease of manufacture
If proprietary algorithms are kept secret, then vendor trade secrets are protected, but user ability to improve model performance deteriorates
Solution Approach 1:
The characterization system serves as a mediator that translates black box predictions into actionable insights. Users can identify which features drive predictions and how to modify them, enabling model performance improvement without requiring access to or understanding of the proprietary algorithm.
Solution Approach 2:
The patent replaces direct access to the algorithmic mechanism with an alternative information gathering approach. Instead of mechanically examining the proprietary code, users can systematically perturb inputs and observe outputs, substituting direct algorithmic access with empirical probing through synthetic data generation.
3Device complexity
If black box models are used, then model complexity and vendor expertise are improved, but transparency and explainability deteriorate
Solution Approach 1:
The system creates a copy or representation of the model's decision-making process through synthetic data analysis. By generating and analyzing synthetic data points with controlled perturbations, the system reconstructs an explanatory model that mirrors the black box behavior in terms of feature importance, making the complex model's decisions interpretable.
Data Source
AI summary
A non-transitory computer-readable medium including instructions, which when executed by one or more processors of a computing system, causes the computing system to: access a machine learning model m, an input data point P to m, P including one or more features, and a prediction m(P) of m for P; create a set of perturbed input data points Pk from P by selecting a new value for at least one feature of P for each perturbed input data point; obtain a prediction m(Pk) for each of the perturbed input data points; analyze the predictions m(Pk) for the perturbed input data points to determine which features are most influential to the prediction; and output the analysis results to a user.