Explainability System for Machine Learning Model Insights
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Solution Overview
Problem
Machine learning models often lack understanding of their own behavior, leading to errors and unintended biases due to unknown relationships and constraints, which can result in undesirable outcomes that may not meet legal or performance standards.
Innovation Solution
The implementation of a system that analyzes and visualizes the features and training data of machine learning models to provide global and local explanations, allowing for the identification of influential features and adjustments to improve model performance, using both model-dependent and model-independent approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are deployed to analyze and classify data, then predictive accuracy and functionality are improved, but understanding and control of model behavior deteriorate
Solution Approach 1:
The patent introduces explainability tools as intermediary components that bridge the gap between the black-box ML model and human understanding. These tools analyze model decisions and present them in interpretable formats, allowing users to understand model behavior without sacrificing predictive accuracy.
Solution Approach 2:
The system implements feedback loops where model predictions are continuously analyzed and explained, and these explanations feed back into the model development process. This allows for iterative improvement of both model accuracy and interpretability through continuous monitoring and adjustment.
2Adaptability or versatility
If complex relationships are encoded in machine learning models, then predictive capability is improved, but unintended biases and errors are introduced
Solution Approach 1:
The patent applies preliminary actions by implementing validation and explanation mechanisms before models are fully deployed. The system pre-analyzes model behavior, identifies potential biases, and validates predictions against expected constraints, preventing unreliable behavior before it occurs.
Solution Approach 2:
The system employs preliminary anti-action by proactively detecting and correcting unintended biases before they cause harmful effects. The explainability tools identify problematic patterns in advance, allowing developers to adjust the model to prevent reliability issues.
3Productivity
If machine learning models operate as black boxes, then processing speed and efficiency are improved, but trustworthiness and compliance are reduced
Solution Approach 1:
The patent segments the model analysis process into distinct components: the core predictive engine that maintains high processing speed, and separate explanation generation modules that provide interpretability. This segmentation allows the main model to operate efficiently while parallel explanation processes enhance trustworthiness without significantly impacting processing speed.
Data Source
AI summary
Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.