Hybrid Explainable AI System for Neural Network Transparency
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Solution Overview
Problem
Existing artificial intelligence systems, particularly deep neural networks, struggle to provide explanations for their decision-making processes, leading to inefficiencies and the need for human intervention to recreate each step.
Innovation Solution
A hybrid explainable artificial intelligence system is developed, combining a shallow learning model based on maximum entropy with a deep learning model. This system inputs data into both models, uses common outputs to formulate explanations for the deep learning model, and provides visual representations to enhance understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep neural network is used to improve prediction accuracy, then the model's predictive capability is enhanced, but the system loses the ability to explain the decision-making process
Solution Approach 1:
The patent introduces an intermediary explanation system that acts as a mediator between the deep neural network and users. This explanation system translates the complex internal representations of the deep network into interpretable forms, allowing users to understand decision-making processes without sacrificing the deep network's predictive accuracy.
Solution Approach 2:
The patent segments the AI system into two distinct components: a deep neural network for accurate predictions and a separate explanation system for interpretability. This segmentation allows each component to specialize in its strength while working together as an integrated hybrid system.
2Loss of information
If human intervention is used to recreate each step of the neural network process to explain outcomes, then explanation accuracy is improved, but resource consumption increases significantly
Solution Approach 1:
The patent implements a self-service explanation mechanism where the AI system automatically generates its own explanations through integrated explanation modules. This eliminates the need for external human intervention to recreate and analyze neural network steps, significantly reducing resource consumption while maintaining explanation quality.
Solution Approach 2:
The patent incorporates explanation capabilities directly into the neural network architecture during the design phase, rather than requiring post-hoc analysis. This preliminary integration allows explanations to be generated automatically as part of the prediction process, reducing the computational resources needed for separate explanation generation.
3Use of energy by moving object
If a shallow learning model is used to simplify the system, then resource consumption is reduced, but the model's predictive capability deteriorates
Solution Approach 1:
The patent merges a shallow learning model and a deep neural network into a hybrid architecture where each component contributes its strengths. The shallow model provides interpretability and handles simpler patterns, while the deep network handles complex predictions, achieving both resource efficiency and high accuracy.
Solution Approach 2:
The patent applies different model complexities to different parts of the problem space. The shallow learning model handles aspects requiring interpretability and simpler patterns, while the deep neural network handles complex predictive tasks. This local differentiation optimizes both resource consumption and prediction accuracy.
Data Source
AI summary
A hybrid explainable artificial intelligence system may include a shallow learning model and a deep learning model. The shallow learning model may be a machine learning system. The deep learning model may be a neural network. The system may input a data set into both the shallow learning model and the deep learning model. Both the shallow learning model and the deep learning model may produce an output. When there is a common output between the shallow learning model and the deep learning model, the process performed by the shallow learning model may be used to formulate an explanation of the process performed by the deep learning model. The explanation of the process performed by the deep learning model may be used to raise the sensitivity of one or more components of the data set. Such components may include a word or phrase within a transcript.