Sparse Multi-Layer Model Explanations Using Latent Activation States
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Solution Overview
Problem
Complex machine learning models, such as neural networks, are difficult to explain due to their non-linear relationships and large number of activation paths, making it challenging to provide deterministic and interpretable explanations, which is essential for regulatory compliance and model development.
Innovation Solution
A method is introduced to generate explanations for multi-layer models by constraining each latent feature to have a limited number of input connections, using activation state matrices to determine activation modes, and selecting explanations based on these states to combine into a deterministic output explanation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex multi-layer neural network models are used to improve predictive accuracy, then prediction performance is improved, but model interpretability deteriorates due to non-linear relationships and large number of activation paths
Solution Approach 1:
The patent segments the complex neural network model into multiple layers (input layer, hidden layers, output layer) and further segments each layer into individual features with specific activation states. By dividing the model into discrete, explainable components and tracing activation paths through each segment, the patent makes the overall complex model interpretable while maintaining its predictive accuracy.
2Device complexity
If traditional explainable models like linear regression or decision trees are used to maintain interpretability, then model transparency is improved, but predictive accuracy deteriorates compared to complex neural networks
Solution Approach 1:
The patent introduces an intermediary explanation generation component that acts as a mediator between the complex neural network model and the user. This intermediary traces activation paths, identifies contributing features, and generates human-understandable explanations without altering the underlying complex model's predictive capabilities. This allows the system to maintain both high predictive accuracy and interpretability.
3Adaptability or versatility
If the number of input connections per latent feature is increased to capture more complex relationships, then model expressiveness is improved, but explanation complexity deteriorates
Solution Approach 1:
The patent applies local quality by allowing different latent features to have different numbers of input connections based on their specific roles in the model. Rather than uniformly limiting all connections, the patent enables each feature to have the appropriate level of connectivity needed for its function, while the explanation system selectively traces only the relevant activation paths, keeping explanations manageable despite varied local complexity.
Data Source
AI summary
A method for generating explanations for a multi-layer model, comprising: accessing a multi-layer model, wherein the multi-layer model comprises an input layer, one or more hidden layers, and an output layer, wherein the input layer comprises a plurality of input features for the multi-layer model, each hidden layer of the one of more hidden layers comprises a plurality of latent features, wherein each latent feature is connected to the input layer or a preceding hidden layer by a limited number of input connections, and the output layer is capable of generating an output based at least in part on activation states of terminal latent features in a final hidden layer, wherein the terminal latent features are a subset of the latent features that are connected directly to the output layer; upon receiving an output generated by the multi-layer model, generating an output explanation.