Explainer Learning Machine for Interpretable Neural Network Analysis
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Solution Overview
Problem
Graph-based learning machines, such as neural networks and Boltzmann machines, pose challenges due to their 'black box' nature, making it difficult to understand and explain their inner workings, which hinders trust, debugging, bias identification, and improvement of these systems.
Innovation Solution
A system comprising a reference learning machine and an explainer learning machine, where the explainer machine provides insights into the importance of components and reasons behind outputs by updating parameters based on test inputs and expected outputs, enabling the explanation of the reference learning machine's decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph-based learning machines are used for complex tasks, then their computational power and accuracy are improved, but their interpretability and understandability deteriorate
Solution Approach 1:
An explainer learning machine is introduced as an intermediary system between the reference learning machine and users. This explainer machine processes outputs from the reference machine and generates human-interpretable explanations, thereby maintaining the high computational power of complex models while improving their interpretability through an intermediate layer of explanation generation.
2Adaptability or versatility
If the structure of learning machines is made complex to handle diverse tasks, then their versatility is improved, but their debuggability and trustworthiness deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the explainer learning machine continuously analyzes outputs from the reference learning machine and provides interpretable feedback about decision-making processes. This feedback loop enables users to understand model behavior, identify bugs, and improve trustworthiness without simplifying the underlying complex structure needed for task versatility.
3Measurement precision
If learning machines process large amounts of data to improve accuracy, then their predictive performance is improved, but their computational resource consumption increases
Solution Approach 1:
The system segments the computational workload into two parts: the reference learning machine that performs heavy computational processing for high accuracy predictions, and the explainer learning machine that processes the outputs to generate interpretations. This segmentation allows the system to maintain high predictive accuracy while reducing the overall computational burden by separating prediction generation from explanation generation.
Data Source
AI summary
Systems, devices and methods are provided for building and using learning machines to understand and explain learning machines. The present system comprises a reference learning machine and an explainer learning machine being built for explaining and understanding the reference learning machine. A set of input signals is fed through the reference learning machine and the outputs at the different components of the learning machine for each given input signal are recorded. The recorded outputs at the different components of the learning machine for each given input signal, along with the corresponding expected output of the learning machine for each given input signal, are then used to update the parameters of the explainer learning machine. After the parameter update process, the explainer learning machine can then be queried for quantitative insights about the reference learning machine.