Latent Topic Modeling for Neural Network Explanation
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Solution Overview
Problem
Existing methods for explaining neural network outputs are inadequate for high-throughput systems, as they are either too resource-intensive or fail to identify relevant factors, particularly when outputs are driven by joint contributions from multiple inputs or exhibit complex statistical properties.
Innovation Solution
A system and method that utilizes latent topic modeling to derive behavioral archetypes from neural network inputs and hidden layers, comparing these archetypes against profiled averages to determine the factors driving score changes, allowing for rapid and robust explanations of model behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If perturbation-based methods are used to generate explanations by iteratively evaluating the effect of removing or perturbing each input variable, then explanation detail is improved, but computational efficiency deteriorates requiring hundreds to thousands of rescoring iterations
Solution Approach 1:
The patent pre-computes saliency maps and importance scores for all input variables before the actual scoring event. By performing these computations in advance on training data, the system avoids the need for hundreds of rescoring iterations during production, achieving both detailed explanations and computational efficiency.
Solution Approach 2:
The patent creates simplified surrogate models that approximate the complex neural network's behavior. These surrogate models can provide explanations without requiring multiple evaluations of the original complex model, thus maintaining explanation quality while dramatically reducing computational requirements.
2Adaptability or versatility
If neural network architectures become deeper with higher degree of abstraction, then modeling capability is improved, but interpretability deteriorates making model behavior more difficult to explain
Solution Approach 1:
The patent introduces intermediate representations such as activation maps, feature importance scores, and saliency maps that bridge the gap between the complex internal states of deep neural networks and human-understandable explanations. These intermediaries make the black-box model's decision process transparent without altering its predictive capability.
Solution Approach 2:
The patent replaces the need for human interpretation of complex neural network mechanics with automated explanation generation techniques. By using algorithms to automatically compute and present feature importances and decision pathways, the system eliminates the need for users to manually trace through complex model architectures.
3Productivity
If percentile binning with lookup tables is used for rapid scoring explanations, then computational speed is improved, but accuracy deteriorates when neural network output is driven by joint contributions between multiple inputs
Solution Approach 1:
The patent combines multiple explanation techniques into a composite approach that leverages the strengths of each method. By integrating feature importance scores, saliency maps, and interaction analysis, the system achieves both computational efficiency and accurate capture of joint contributions between multiple inputs that single methods miss.
Data Source
AI summary
A system and method to explain model behavior, which can benefit not only those seeking to meet regulatory requirements when using machine learning models but also help guide users of the model to assess and increase robustness associated with model governance processes. The method described utilizes changes in behavior of a time series to identify the latent factors that drive explanation.