Real-Time Explainability Overlay for Sensor ML Operator Assurance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models in sensor-based remote sensing systems are often regarded as 'black boxes,' leading to a lack of explainability and trust among operators, which can hinder their deployment and adoption, especially in critical applications.
Innovation Solution
Integrating visualizations of sensor data with explainability data that indicate which features the model relies upon for inference, allowing operators to understand the decision-making process intuitively and providing tools for feedback and retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to process sensor data, then analysis speed and accuracy are improved, but explainability and operator trust deteriorate
Solution Approach 1:
The patent introduces explainability visualizations as an intermediary between the machine learning model and the operator. These visualizations translate the model's internal decision-making process into intuitive graphical representations that show which sensor data features influenced the model's output, thereby maintaining both high analysis speed and operator understanding
Solution Approach 2:
The system implements feedback loops where operator interactions with the explainability visualizations (such as annotating sensor data or providing corrections) are used to retrain and refine the machine learning model. This continuous feedback mechanism improves model performance while maintaining transparency about the decision-making process
2Measurement precision
If complex machine learning models are deployed, then detection accuracy is improved, but operator trust and adoption deteriorate
Solution Approach 1:
The patent employs visual explanation tools as mediators that bridge the gap between complex model operations and operator understanding. By displaying which sensor features the model focuses on during decision-making, these visualizations make the model's behavior interpretable without reducing its complexity or accuracy
Solution Approach 2:
The system enables operators to actively engage with the model through feedback mechanisms where they can annotate sensor data, correct model predictions, and provide training examples. This self-service approach allows operators to directly improve model performance while gaining deeper understanding of its operation
3Ease of operation
If explainability visualizations are added to the interface, then operator understanding is improved, but interface complexity increases
Solution Approach 1:
The patent merges the explainability visualizations with the existing sensor data display by overlaying them in the same graphical interface space. This integration allows operators to view both the raw sensor data and the model's focus areas simultaneously without requiring separate windows or complex navigation, thereby improving understanding while minimizing interface complexity
Data Source
AI summary
A computer-implemented method includes transforming sensor data into a first spatial representation, transforming a graphical user interface to display the first spatial representation, transforming the sensor data into a second spatial representation, providing the second spatial representation as input features to a machine learning model to generate inference data, providing the input features, parameters of the machine learning model, and the inference data to an explainability model to generate explainability data, transforming the explainability data into a third spatial representation, the third spatial representation being in a same space as the first spatial representation, and transforming the graphical user interface to overlay the third spatial representation on the first spatial representation.