Interactive Machine Learning Model Feedback Loop
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Solution Overview
Problem
Current machine learning applications in industries often lack direct user involvement, and users do not have the opportunity to influence predicted outcomes, leading to a lack of trust and understanding in the 'black box' nature of machine learning models.
Innovation Solution
Systems and methods that allow users to interact with machine learning models by changing input data or retraining the model using human feedback, enabling users to influence predictions directly through graphical user interfaces or data file amendments, using machine learning modules for preprocessing, training, and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used for predictions without user involvement, then prediction speed and automation are improved, but user trust and understanding of the model deteriorate
Solution Approach 1:
The system implements feedback loops where users can provide corrections to model predictions, and this feedback is used to retrain and improve the model. Users can amend predictions by moving objects on the screen or modifying data files, creating a continuous improvement cycle that builds trust while maintaining automation.
Solution Approach 2:
The patent introduces an interactive interface as an intermediary between the black-box model and the user. This interface allows users to visualize and modify predictions without needing to understand the underlying model complexity, bridging the gap between automation and user control.
2Device complexity
If machine learning models operate as black boxes without user interaction, then model complexity is reduced, but user understanding and control deteriorate
Solution Approach 1:
An interactive graphical user interface serves as a mediator that simplifies user interaction with complex machine learning models. Users can move objects on the screen or edit data files to influence predictions without needing to understand the underlying model architecture or algorithms.
Solution Approach 2:
The system allows users to directly amend predictions and influence model behavior through simple interactions like moving objects on a screen or modifying data files. The model adapts to user preferences through feedback, enabling users to control outcomes without technical expertise.
3Reliability
If users are given the opportunity to influence predictions through feedback, then user trust and model accuracy are improved, but system complexity and training time increase
Solution Approach 1:
The system incorporates feedback mechanisms where user corrections to predictions are captured and used to retrain the model. This continuous feedback loop improves model accuracy over time while maintaining a relatively simple system architecture through iterative refinement.
Solution Approach 2:
The model is pre-trained on initial datasets before deployment, establishing a baseline level of accuracy. Subsequent user feedback then builds upon this foundation through incremental retraining, rather than requiring complete system redesign.
4Measurement precision
If users can amend predictions and retrain models, then prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
Instead of complete retraining, the system uses partial retraining where only the necessary model components are updated based on user feedback. This selective approach improves accuracy while minimizing the time and computational resources required compared to full model retraining.
Solution Approach 2:
The model undergoes preliminary training on comprehensive datasets before deployment, creating a robust baseline. Subsequent user feedback then requires only incremental updates rather than complete retraining, reducing the time investment for improvements.
Data Source
AI summary
A computer-implemented method of interactive machine learning in which a user is provided with predicted results from a trained machine learning model. The user can take the predicted results and either: i) adjust the predicted results and input the adjusted results as new data; or ii) adjust the predicted data to retrain the model.