Multi-observer Consensus Ground Truth for AI Drift
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Solution Overview
Problem
Artificial Intelligence (AI) and Machine Learning (ML) models experience performance degradation due to data drift and concept drift, especially in Edge computing environments, where conventional monitoring methods require significant human intervention and rely on ground truth data that is often unavailable in real-world scenarios.
Innovation Solution
The system detects observation overlap between multiple devices, identifies consensus in AI/ML model inferences, and tags data with ground truth labels, using hardware characteristics and confidence adjustments to mitigate drift, enabling automated re-training and lifecycle management of AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring methods are used to detect AI/ML model drift, then ground truth data can be obtained for model validation, but significant human intervention is required and the process is not scalable to real-world Edge computing environments
Solution Approach 1:
The system enables automated drift detection and ground truth generation by having the monitoring system itself perform the validation tasks. Multiple AI/ML models independently analyze the same data stream, and their consensus results automatically serve as ground truth without requiring external human annotation or intervention.
Solution Approach 2:
The patent introduces an intermediary consensus mechanism that mediates between multiple AI/ML model inferences. This consensus layer acts as a mediator to aggregate individual model predictions and generate reliable ground truth labels, eliminating the need for direct human involvement in the validation process.
2Extent of automation
If multiple AI/ML models are deployed to detect drift through consensus, then ground truth can be generated without human intervention, but the device complexity and computational resources increase
Solution Approach 1:
The system segments the drift detection task into multiple independent AI/ML models, each analyzing the data stream separately. This segmentation allows parallel processing and distribution of computational load, reducing the complexity burden on any single component while maintaining automated ground truth generation through consensus aggregation.
3Reliability
If AI/ML models are re-trained frequently to mitigate drift, then model performance is maintained, but the re-training process consumes significant time and computational resources
Solution Approach 1:
The system performs preliminary drift detection using multiple models in production before re-training is triggered. By continuously monitoring for drift conditions and preparing candidate training data in advance, the system can initiate re-training only when necessary, reducing unnecessary re-training cycles and associated time losses while maintaining model reliability.
Data Source
AI summary
Embodiments of systems and methods for multi-observer, consensus-based ground truth are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: detect an observation overlap between two or more devices; identify a consensus between Artificial Intelligence (AI) or Machine Intelligence (ML) model inferences made based upon data received by the two or more devices; and in response to the identification, tag at least a subset of the data with a ground truth label.