Neural-Backed Decision Tree Map for Local Interpretability
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Solution Overview
Problem
Current AI interpretability techniques, such as ante-hoc and post-hoc methods, face limitations in providing clear explanations of neural network decisions, often relying on unsuitable linear models or disconnecting interpretation from the training process, which fails to meet the need for coarse-grained data explanations.
Innovation Solution
A neural-backed decision tree architecture is introduced, where each transform layer of a neural network generates a neural-backed decision tree map that translates output data into a list of interpretable words from a generative search domain of facts and evidence, preserving the ambiguity of deep learning models while providing local interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If ante-hoc interpretability techniques use linear models (e.g., decision trees) to ensure interpretability at the beginning, then interpretability is improved, but model accuracy deteriorates because linear models do not have the same accuracy as deep learning models
Solution Approach 1:
The patent divides the deep learning model into multiple transform layers and generates separate neural-backed decision trees for each layer. This segmentation allows each local interpretation to capture the behavior of individual layers using simpler models while the overall system maintains deep learning accuracy through the combination of multiple layer interpretations.
Solution Approach 2:
The patent introduces neural-backed decision trees as intermediary models that bridge the gap between complex deep learning models and human interpreters. These decision trees serve as mediators that translate the internal representations of deep learning layers into interpretable formats without requiring the entire model to be replaced with a linear model.
2Loss of information
If post-hoc interpretability techniques (e.g., LIME, LRP) are used to probe the model with new input data, then interpretability is provided, but there is a disconnect between the interpretation method and the training process, making interpretation difficult to extend
Solution Approach 1:
The patent generates neural-backed decision trees during the training process itself, rather than as a separate post-hoc step. This preliminary action integrates interpretation capability into the training workflow, ensuring that the interpretation method is aligned with the training process and can be extended to new data and models seamlessly.
Solution Approach 2:
The patent merges the training process with the interpretation generation process by creating neural-backed decision trees that are trained alongside the deep learning model. This combination eliminates the disconnect between training and interpretation, allowing both to evolve together and improving adaptability to new scenarios.
3Loss of information
If a full and complete explanation of the machine learning model is generated, then comprehensive interpretability is achieved, but the use of decision and linear models is forced regardless of whether these approaches are suitable for a particular application
Solution Approach 1:
The patent provides local interpretability by generating neural-backed decision trees for individual transform layers rather than forcing a single global linear model. This local approach allows each layer to be interpreted with the most suitable model type for that specific layer's function, improving overall suitability for different applications while maintaining comprehensive coverage.
Solution Approach 2:
The patent employs a dynamic interpretation strategy where neural-backed decision trees are generated adaptively for each transform layer based on its specific characteristics. This dynamic approach allows the interpretation method to adjust to the needs of each layer and application, rather than applying a static linear model uniformly across all layers.
Data Source
AI summary
A computer-implemented process for training a neural network having a plurality of transform layers includes the following operations. Input data for one transform layer of the plurality of transform layers is transformed by the one transform layer into output data. A neural-backed decision tree is generated for the transform layer, a neural-backed decision tree. The transforming and the generating are repeated for each of the plurality of transform layers. A neural-backed decision tree map for a particular one of the plurality of transform layers maps output data of the particular one of the plurality of transform layers into a list of interpretable words from a generative search domain of facts and evidence.