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 their decision-making processes.
Innovation Solution
Implementing a combination of linear and non-linear surrogate models to approximate and explain the predictions of machine learning models, providing reason codes and feature importance, thereby increasing transparency and trust through reduced computational resources and improved debugging capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to make predictions, then prediction accuracy is improved, but transparency and interpretability deteriorate due to the black box nature of complex functions
Solution Approach 1:
The patent introduces surrogate models as intermediary systems that mediate between the complex machine learning model and the user. These surrogate models approximate the behavior of the complex model while providing interpretable explanations, thus preserving prediction accuracy while restoring transparency through reason codes and feature importance metrics.
Solution Approach 2:
The patent creates simplified copies (surrogate models) of the complex machine learning model. These copies replicate the predictive functionality while being designed to be interpretable, allowing users to understand the decision-making process without sacrificing the predictive power of the original complex model.
2Reliability
If proprietary software is used to maintain prediction accuracy, then model performance is preserved, but user control and debugging capabilities are reduced
Solution Approach 1:
The patent enables users to serve themselves by providing tools that allow independent model interpretation and debugging. Through the surrogate models and explanation interfaces, users can independently analyze model behavior, understand predictions, and debug issues without requiring proprietary software or expert assistance, thus maintaining model performance while enhancing user control.
3Adaptability or versatility
If complex machine learning models are deployed, then predictive power is enhanced, but computational resources and debugging difficulty increase
Solution Approach 1:
The patent segments the complex machine learning model into multiple interpretable components through surrogate models. By dividing the complex predictive function into simpler, explainable parts that can be analyzed individually, the system maintains overall predictive power while reducing the computational complexity required for interpretation and debugging.
Data Source
AI summary
An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.