Model Output Explanation via Feature Contribution Clustering
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Solution Overview
Problem
Complex machine learning models, such as ensembled models, are difficult to explain, making it challenging for businesses to provide transparent decision-making processes and comply with regulations like the Fair Credit Reporting Act, which requires reasons for credit application denials to be understandable by consumers.
Innovation Solution
A system and method for generating output-specific explanation information by identifying feature groups with similar contributions to model outputs, assigning human-readable explanations, and providing these explanations to consumers, using techniques like feature contribution analysis and similarity metrics to cluster features and generate understandable explanations for model decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex machine learning models (such as ensembled models) are used to improve predictive accuracy and decision-making capability, then model performance is improved, but model interpretability and explanation capability deteriorate
Solution Approach 1:
The patent introduces an intermediary explanation system that mediates between the complex machine learning model and the end user. This intermediary layer generates human-readable explanations by analyzing feature contributions and relationships, translating complex model decisions into understandable narratives without altering the underlying complex model structure.
Solution Approach 2:
The patent replaces the need for direct human understanding of complex model mechanics with an automated explanation generation system. Instead of requiring users to comprehend complex ensembled model operations, the system automatically generates explanations by analyzing feature contributions, relationships, and interactions, substituting mechanical understanding with information-based explanations.
2Measurement precision
If complex machine learning models are used to improve decision accuracy, then predictive capability is improved, but the ability to provide transparent explanations for regulatory compliance deteriorates
Solution Approach 1:
The explanation system acts as an intermediary that captures and translates model decision information into transparent explanations. It analyzes feature contributions, relationships, and interactions to generate comprehensive explanations that maintain the accuracy benefits of complex models while providing the transparency required for regulatory compliance.
Solution Approach 2:
The system performs preliminary analysis of model decisions by examining feature contributions and relationships before generating explanations. This preliminary action involves computing feature importance metrics, identifying key relationships, and preparing explanation content in advance, ensuring that transparent explanations are available when needed for regulatory compliance.
3Loss of information
If detailed feature-level explanations are provided to improve explanation completeness, then explanation quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the explanation generation process into distinct components: feature contribution analysis, relationship identification, and narrative generation. This segmentation allows the system to process different aspects of model decisions separately and efficiently, reducing overall computational complexity while maintaining explanation completeness.
Solution Approach 2:
The system applies local quality by providing detailed explanations for specific features and relationships that are most relevant to each individual prediction. Rather than uniformly analyzing all features at maximum detail, the system focuses computational resources on the most influential features and relationships, maintaining explanation quality while reducing processing time.
Data Source
AI summary
Systems and methods for explaining models.