Visualizing ML Token Influence via Color-Coded GUI
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Solution Overview
Problem
Machine-learning models are often considered 'black boxes,' making it difficult for users to understand how they produce outputs, which can be a challenge in decision-making processes and may violate regulatory requirements, especially as models grow in size and complexity.
Innovation Solution
A graphical user interface is developed that includes visualizations to indicate contributing factors to a machine-learning model's output, such as a hierarchical list of tokens from a textual dataset, color-coded to show their influence on the model's classification or categorization, helping users understand the decision-making process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning models are made larger and more complex to improve accuracy, then model performance is improved, but interpretability and user understanding deteriorate
Solution Approach 1:
The patent introduces an intermediary visualization system that sits between the complex machine-learning model and the user. This system captures intermediate outputs (token-level classifications, attention weights, probability distributions) and transforms them into interpretable visual representations. The intermediary layer preserves the complexity of the model internally while presenting simplified, human-understandable outputs externally, thus resolving the contradiction between model accuracy and interpretability.
2Loss of information
If detailed visualizations of model decision-making are provided, then user understanding is improved, but system complexity increases
Solution Approach 1:
The patent segments the visualization system into distinct modular components: token processing modules, classification modules, visualization generation modules, and interaction handlers. Each segment handles a specific aspect of the visualization process independently. This segmentation allows the system to manage complexity through modularity while providing comprehensive visualizations, as each component can be developed, tested, and maintained separately.
3Loss of information
If comprehensive token-level analysis is performed to identify contributing factors, then model interpretability is improved, but computational resources increase
Solution Approach 1:
The patent implements partial action by selectively analyzing only the most relevant tokens rather than processing every token uniformly. The system identifies and focuses computational resources on tokens with higher impact scores or those that contribute most to the final classification. This approach provides sufficient interpretability for understanding model decisions while avoiding the excessive computational cost of analyzing every single token in detail.
Data Source
AI summary
In one example, a system can execute a first machine-learning model to determine an overall classification for a textual dataset. The system can also determine classification scores indicating the level of influence that each token in the textual dataset had on the overall classification. The system can select a first subset of the tokens based on their classification scores. The system can also execute a second machine-learning model to determine probabilities that the textual dataset falls into various categories. The system can determine category scores indicating the level of influence that each token had on a most-likely category determination. The system can select a second subset of the tokens based on their category scores. The system can then generate a first visualization depicting the first subset of tokens color-coded to indicate their classification scores and a second visualization depicting the second subset of tokens color-coded to indicate their category scores.