Expert-in-the-loop ML Training via Explainability Feedback
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Solution Overview
Problem
Current machine learning tools face challenges in automating feature engineering and incorporating domain expertise for model training, particularly in real-time data scenarios, due to the complexity of deriving optimal features and the difficulty in imparting human knowledge to models effectively.
Innovation Solution
The method provides online expert-in-the-loop training for machine learning models, allowing domain experts to interact with the training process through feedback mechanisms, using explainability operations to adjust and update models based on expert input, and enabling continuous learning and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional machine learning tools are used for model training, then automation is achieved, but domain expertise cannot be effectively incorporated
Solution Approach 1:
The patent implements a feedback mechanism where domain experts provide iterative feedback on model explanations and predictions. This feedback loop allows expert knowledge to continuously refine the model without requiring full manual retraining, thus maintaining automation while incorporating domain expertise. The system learns from expert corrections and adjusts its feature engineering and model behavior accordingly.
Solution Approach 2:
The patent introduces an intermediary layer between automated machine learning tools and domain experts. This intermediary consists of explainability operations that translate model decisions into human-understandable explanations, allowing experts to provide meaningful feedback. The intermediary enables communication between the automated system and human expertise without requiring direct manual intervention in the training process.
2Measurement precision
If feature engineering is performed manually to incorporate domain knowledge, then model accuracy improves, but time and effort increase significantly
Solution Approach 1:
The patent performs preliminary feature engineering automatically using machine learning algorithms before expert review. This preliminary action generates initial features that capture basic patterns in the data, reducing the amount of manual feature engineering needed. Experts then focus their time on refining and adjusting these pre-generated features rather than creating them from scratch.
Solution Approach 2:
The system enables self-service feature engineering where the machine learning model automatically generates and refines features based on the data patterns it detects. The model serves itself by identifying relevant features without constant human intervention, freeing experts to focus only on critical adjustments and domain-specific refinements rather than manual feature creation.
3Measurement precision
If complex feature engineering is performed to handle high-dimensional datasets, then model performance improves, but system complexity increases
Solution Approach 1:
The patent extracts and separates the feature engineering function from the overall model training system. By isolating feature engineering as a distinct, automated component, the system reduces complexity in the main training pipeline. Experts can interact with this extracted feature engineering module through simple feedback mechanisms rather than navigating complex system configurations.
Solution Approach 2:
The patent replaces manual, mechanical feature engineering processes with automated machine learning algorithms. Instead of experts manually configuring complex features, the system uses computational algorithms to automatically discover and engineer features from high-dimensional data, reducing system complexity while maintaining or improving performance.
4Measurement precision
If offline training is used to incorporate expert feedback, then model accuracy improves, but real-time adaptability is lost
Solution Approach 1:
The patent implements periodic feedback cycles where expert input is incorporated at regular intervals rather than requiring continuous offline retraining. The model operates in real-time using its current knowledge, periodically pausing to incorporate expert feedback and updates. This periodic action maintains real-time adaptability while systematically integrating expert knowledge to improve accuracy over time.
Solution Approach 2:
The system transitions from static offline training to dynamic online learning where the model can adapt in real-time. The feedback mechanism is designed to work incrementally, allowing the model to dynamically adjust its behavior based on expert input without requiring complete retraining. This dynamic approach enables the system to maintain real-time performance while continuously improving through expert guidance.
Data Source
AI summary
Embodiments for providing expert-in-the-loop training of machine learning models in a computing environment by a processor. A performance of a machine learning model may be learned. Feedback for the machine learning model may be received based on learning the performance the machine learning model, where the feedback includes domain knowledge provided by a domain expert. The machine learning model may be trained or updated based the feedback of the performance of the machine learning model.