Secure ML Feedback Mechanism for Model Accuracy
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Solution Overview
Problem
Current decision support systems face errors due to incomplete training data, misinterpretation, and contradictory domain expert feedback, leading to inefficient resource consumption and incorrect results, as they struggle to collect usable domain expert feedback effectively.
Innovation Solution
A secure and collaborative feedback mechanism is implemented, where a decision support system receives and processes machine learning models, user input, and feedback, determining agreement between prediction and explanation feedback based on a threshold, updating the model, and cryptographically protecting it to prevent tampering and enable transparent historical updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain expert feedback is collected for training machine learning models, then model accuracy and reliability are improved, but resource consumption increases and contradictory feedback leads to errors
Solution Approach 1:
The system performs preliminary actions by collecting and storing domain expert feedback annotations in advance, creating a feedback database before model training. This allows the model to be trained on pre-processed, organized feedback data rather than processing raw feedback in real-time, reducing computational resource consumption during training while maintaining model accuracy.
Solution Approach 2:
The patent introduces an intermediary mechanism - a feedback processing system that mediates between domain expert feedback and machine learning model training. This intermediary layer validates, reconciles contradictory feedback, and transforms raw feedback into structured training data, reducing errors from contradictory feedback while optimizing resource usage through efficient feedback management.
2Productivity
If machine learning models are updated with domain expert feedback, then system performance improves over time, but incomplete or incorrect data introduces errors
Solution Approach 1:
The system implements a feedback mechanism where domain expert annotations are collected, validated, and used to update machine learning models. The feedback loop includes validation steps to ensure data quality, reconciliation processes to handle contradictory feedback, and iterative model updates that improve performance while maintaining reliability through controlled feedback processing.
Solution Approach 2:
Before updating models with feedback data, the system performs preliminary validation and processing of the feedback to ensure completeness and correctness. This includes checking data quality, resolving contradictions, and preparing training data in advance, which prevents incomplete or incorrect data from introducing errors into the model updates.
3Adaptability or versatility
If feedback from multiple domain experts is collected, then comprehensive coverage is improved, but agreement between experts becomes difficult to achieve
Solution Approach 1:
The patent introduces an intermediary feedback processing system that mediates between multiple domain experts' annotations. This intermediary layer provides structured templates for feedback collection, automated validation rules, and reconciliation mechanisms that handle contradictions systematically, making it easier to collect comprehensive feedback while managing the complexity of achieving expert agreement.
Solution Approach 2:
The system changes parameters by introducing structured feedback templates, validation thresholds, and agreement criteria. These parameter changes transform unstructured expert opinions into standardized data with defined quality metrics, enabling comprehensive feedback collection while providing clear guidelines for resolving disagreements and achieving expert consensus.
Data Source
AI summary
A device may receive, from a user device, a machine learning model, training data, and user input for the machine learning model, and may process the training data and the user input, with the machine learning model, to generate a prediction and an explanation of the prediction. The device may provide the prediction and the explanation to the user device and may receive, from the user device, prediction feedback for the prediction and explanation feedback for the explanation. The device may determine whether an agreement is achieved between the prediction feedback and the explanation feedback based on a threshold and may update the machine learning model based on the agreement being achieved. The device may cryptographically protect the updated machine learning model to generate an updated and cryptographically protected machine learning model and may perform actions based on the updated and cryptographically protected machine learning model.