Surrogate Models for Interpretable Machine Learning Predictions
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Solution Overview
Problem
Machine learning models lack transparency and accountability as they do not provide clear explanations for their predictions, forcing users to rely on proprietary software and limiting the information output, which hinders understanding and trust in the decision-making process.
Innovation Solution
Implementing a combination of linear and non-linear surrogate models to approximate and explain the machine learning model's predictions, providing reason codes and feature importance, thereby increasing transparency and accountability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex machine learning model is used to make predictions, then prediction accuracy is improved, but model transparency and interpretability deteriorate
Solution Approach 1:
The patent introduces surrogate models as intermediary systems that mediate between the complex black-box machine learning model and the user. These surrogate models (such as decision trees or linear models) are trained to approximate the predictions of the complex model while being inherently interpretable. The surrogate model serves as a transparent intermediary that provides both predictions and explanations for those predictions, thus resolving the contradiction between accuracy and transparency.
Solution Approach 2:
The patent segments the interpretation function into separate components: the complex model handles prediction accuracy while the surrogate model handles interpretability. This segmentation allows each component to specialize - the complex model optimizes for accurate predictions while the simpler surrogate model optimizes for human-understandable explanations, thereby resolving the trade-off between these two competing requirements.
2Measurement precision
If proprietary machine learning software is used, then prediction capability is improved, but user control and understanding deteriorate
Solution Approach 1:
The patent enables the system to explain its own predictions through surrogate models that are derived from the complex model's behavior. The surrogate models provide self-explanation capabilities, allowing the system to autonomously generate interpretable outputs without requiring external proprietary tools or services. This self-service approach gives users direct control and understanding of the model's decision-making process.
3Loss of information
If detailed explanations are provided for predictions, then model transparency is improved, but computational resources and time increase
Solution Approach 1:
The patent employs lightweight surrogate models that are computationally inexpensive compared to the complex original model. These surrogate models can be quickly trained and evaluated, providing explanations without the heavy computational burden of analyzing the complex model's internal workings. The surrogate models act as disposable, low-cost approximation tools that deliver transparency efficiently.
Solution Approach 2:
The patent changes the complexity parameter of the explanation model by using simpler model structures (such as shallow decision trees or linear models) as surrogates. This parameter change reduces computational requirements while maintaining the essential predictive behavior, thereby providing transparency without excessive computational cost.
Data Source
AI summary
Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.