Explainable Neural Network Architecture for Multidimensional Data
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Solution Overview
Problem
Current AI systems, particularly convolutional neural networks (CNNs), operate as black-box models, making it difficult to explain their decision-making processes, which limits their adoption in critical applications like autonomous driving and the medical industry due to lack of interpretability.
Innovation Solution
The integration of convolutional layers into explainable neural networks (XNNs) allows for the creation of a fully white-box system that combines convolutional layers with a general XNN architecture, enabling seamless processing of multidimensional data and providing structured explanations through a reverse indexing mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks (CNNs) are used for image, video, and speech recognition, then processing efficiency and accuracy are improved, but interpretability and explainability deteriorate due to the black-box nature of the models
Solution Approach 1:
The patent segments the neural network into distinguishable components: convolutional layers for feature extraction and an explainable neural network layer for interpretable processing. This segmentation allows the system to maintain the high accuracy of CNNs while introducing interpretability through the separate XNN layer that provides structured explanations for decisions.
Solution Approach 2:
The explainable neural network layer acts as an intermediary between the convolutional layers and the final output. It receives features from the CNN, processes them through interpretable operations, and generates both predictions and explanations, thereby mediating between the black-box CNN processing and the need for transparent decision-making.
2Device complexity
If traditional multilayer perceptron models are used, then model simplicity is maintained, but processing efficiency and accuracy deteriorate due to full connectivity between nodes
Solution Approach 1:
The patent merges the advantages of simple MLP structures with the efficiency of CNNs by combining convolutional layers (which provide computational efficiency and spatial structure awareness) with an explainable neural network layer (which maintains interpretability). This hybrid approach achieves both processing accuracy and model manageability.
Solution Approach 2:
The explainable neural network layer introduces local quality by providing specific, localized explanations for different parts of the input data. Instead of treating all inputs uniformly, the system identifies and explains specific features and their contributions to the prediction, making the model both accurate and locally interpretable.
3Loss of information
If convolutional layers are integrated into explainable neural networks, then interpretability is improved through white-box architecture, but device complexity increases due to the combination of multiple network components
Solution Approach 1:
The explainable neural network layer is designed with multi-functionality, serving both as a processing layer that continues the feature extraction task and as an explanation layer that provides interpretability. By making this single layer universal, the system avoids the need for separate, dedicated explanation modules, thereby reducing overall architectural complexity while maintaining explainability.
Solution Approach 2:
The patent employs a nested architecture where the explainable neural network layer is integrated within the broader CNN framework. The XNN layer is nested after the convolutional layers, allowing it to inherit and build upon the features extracted by the CNN while adding interpretability capabilities. This nested structure avoids redundant components and streamlines the overall architecture.
4Loss of information
If post-hoc explanation methods are applied to black-box models, then some level of interpretability is achieved, but the explanations are derived locally with limited access to inner workings of the model
Solution Approach 1:
The explainable neural network layer performs preliminary processing of the input data through the same convolutional operations used in the main network, ensuring that the data is transformed into the same feature representation space. This preliminary action ensures that subsequent explanations are based on the actual internal representations of the model, providing reliable and accurate explanations that reflect the true decision-making process.
Solution Approach 2:
The system incorporates feedback mechanisms where the explainable neural network layer receives not only the input data but also the intermediate features from the convolutional layers. This feedback loop allows the XNN layer to align its explanations with the actual computational paths taken by the CNN, ensuring that the explanations accurately reflect the model's inner workings rather than being merely post-hoc approximations.
Data Source
AI summary
An exemplary embodiment may describe a convolutional explainable neural network. A CNN-XNN may receive input, such as 2D or multi-dimensional data, a patient history, or any other relevant information. The input data is segmented into various objects and a knowledge encoding layer may identify and extract various features from the segmented objects. The features may be weighted. An output layer may provide predictions and explanations based on the previous layers. The explanation may be determined using a reverse indexing mechanism (Backmap). The explanation may be processed using a Kernel Labeler method that allows the labelling of the progressive refinement of patterns, symbols and concepts from any data format that allows a pattern recognition kernel to be defined allowing integration of neurosymbolic processing within CNN-XNNs. The optional addition of meta-data and causal logic allows for the integration of connectionist models with symbolic logic processing.