Graphical Explanations for Interpreting ML Predictions
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Solution Overview
Problem
Complex machine learning models, such as neural networks, lack transparency in their decision-making processes, leading to mistrust and difficulty in understanding and verifying their predictions, which is particularly problematic in critical applications like healthcare and security.
Innovation Solution
A method and system that generates graphical explanations of machine learning predictions by analyzing both active and passive attributes of a query, using clustering algorithms and Generative Adversarial Networks (GANs) to create contextual images and narratives that clarify the reasoning behind model outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models like neural networks are used to achieve high performance on cognitive tasks, then prediction accuracy is improved, but transparency and interpretability of the decision-making process deteriorate
Solution Approach 1:
The patent introduces an explanation generation system as an intermediary between the complex neural network model and the user. This mediator takes the model's predictions and generates human-interpretable explanations that reveal the decision-making process, thereby recovering the lost transparency without affecting the model's predictive accuracy.
Solution Approach 2:
The explanation generation process segments the complex decision-making process into understandable components by analyzing active features and generating narratives about how each feature contributed to the prediction. This segmentation makes the black-box model's reasoning visible and interpretable.
2Reliability
If complex machine learning models are deployed in critical applications like healthcare and security, then predictive capability is improved, but trust and verifiability of predictions deteriorate
Solution Approach 1:
The system provides feedback to users by generating explanations that reveal how predictions were made. This feedback loop allows users to verify predictions, understand the reasoning, and build trust in the model's decisions, particularly important for critical applications where accountability is essential.
Solution Approach 2:
The explanation generation system acts as a mediator that bridges the gap between complex model predictions and human trust. By providing verifiable explanations, it enables users to assess the reliability of predictions without needing to understand the complex internal workings of the neural network.
3Loss of information
If detailed analysis of both active and passive attributes is performed to generate explanations, then interpretability is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs partial analysis by focusing primarily on active features that directly influence predictions, while using passive features contextually. This selective approach provides sufficient interpretability without requiring exhaustive analysis of all possible attributes, thereby managing computational complexity.
Solution Approach 2:
The explanation generation applies different levels of analysis to different features based on their relevance. Active features receive detailed analysis while passive features are used more contextually, creating a locally optimized explanation process that balances interpretability with computational efficiency.
Data Source
AI summary
Query data associated with a query is detected, where the query data includes an active feature and a passive attribute used in a machine learning model, and where the machine learning model is trained to output a predicted result based at least in part on the active feature. Using a first trained neural network, a digital image is generated based at least in part on the passive attribute, and from the machine learning model outcome narrative data indicative of an explanation of the predicted result and based at least in part on the active feature is also generated. Then, using an image generator, a graphical explanation of the predicted result is generated, the graphical explanation being based at least in part on the outcome narrative data and the digital image.