Adversarial Annotation Feedback for ML Training Data Quality
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Solution Overview
Problem
Current artificial intelligence and machine learning systems require labor-intensive and expensive training processes to maintain high-quality and up-to-date training data, which are prone to human biases and logical inconsistencies.
Innovation Solution
An adversarial annotation system that compares machine learning model outputs to user task data, generates annotations based on differences, and retrains the model using these annotations to improve accuracy and reduce biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually annotate training data, then annotation quality and accuracy are improved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables machine learning models to annotate their own outputs by comparing model predictions against ground truth labels and automatically generating corrections. This self-service mechanism reduces reliance on manual human annotation while maintaining high quality through iterative improvement cycles.
Solution Approach 2:
The system implements a feedback loop where model outputs are compared with ground truth, differences are identified, annotations are generated from these differences, and the model is retrained using the new annotations. This continuous feedback mechanism improves annotation quality over time while reducing manual intervention requirements.
2Reliability
If more training data is collected to improve model accuracy, then model performance improves, but data collection cost and complexity increase
Solution Approach 1:
The system automatically generates training data annotations by having the model compare its own outputs against ground truth and create corrective annotations. This self-service approach eliminates the need for complex manual data collection processes while still producing high-quality training data for model improvement.
Solution Approach 2:
The system recovers valuable annotation information from model errors and uses this recovered data to generate training annotations. Instead of discarding model outputs that are incorrect, the system extracts the error information and transforms it into useful training data, reducing the need for extensive new data collection.
3Adaptability or versatility
If training data is continuously updated to reflect current information, then model up-to-dateness improves, but data production cost increases
Solution Approach 1:
The system continuously updates training data by comparing current model outputs with ground truth and generating new annotations from the differences. This feedback-driven approach enables continuous model adaptation to current information without requiring expensive manual data production processes.
Solution Approach 2:
The model itself performs the data production function by automatically generating updated annotations from its own performance data. This self-service mechanism eliminates the need for external manual data production while maintaining continuous up-to-dateness of the training data.
Data Source
AI summary
Systems and methods for operating an artificial intelligence and machine learning model to generate annotations of data and to generate training datasets is disclosed. One disclosed system includes one or more processors configured to: assign a task to a machine learning model; receive an output from the machine learning model associated with the task; compare the output to task data associated with a user performing the assigned task; and when there is a difference between the output of the machine learning model and the task data, generate an annotation.