Informativeness Score for Selective Human Annotation
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Solution Overview
Problem
The quality and efficiency of machine learning model training are hindered by the labor-intensive and costly process of human annotation, especially in large datasets, where active learning algorithms often fail to provide adequate label quality and quantity, leading to performance degradation in imbalanced problems.
Innovation Solution
A method and system that automatically determine which data samples require human annotation by assigning an informativeness score, prioritizing them for annotation based on computational analysis, and matching them with expert annotators based on competency and preference, using AI algorithms for label unification and competency adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data samples are annotated by multiple human annotators to increase label quality, then annotation quality improves, but annotation time and cost increase significantly
Solution Approach 1:
The patent segments the annotation process into two distinct phases: (1) automated pre-annotation using machine learning models to generate initial labels, and (2) selective human verification where annotators only review and correct uncertain or low-confidence predictions. This segmentation allows most samples to be processed quickly by machines while human expertise is concentrated only on problematic cases, thereby maintaining high annotation quality while dramatically reducing overall annotation time and cost.
Solution Approach 2:
The patent introduces an automated confidence assessment mechanism as an intermediary between the machine learning model and human annotators. This intermediary evaluates the certainty of automated predictions and selectively routes only uncertain cases to human reviewers. By acting as a filter and triage system, the intermediary ensures that human annotators focus their efforts on cases where their input is most valuable, optimizing the trade-off between annotation quality and time investment.
2Quantity of substance
If new data samples are annotated to increase the total number of labeled instances, then model training data quantity increases, but annotation resources are depleted
Solution Approach 1:
The patent applies preliminary action by implementing automated pre-annotation using trained machine learning models before human annotation occurs. The system generates initial labels for all data samples, and human annotators only need to verify or correct these pre-generated labels. This preliminary automated processing dramatically increases the effective throughput of annotation, allowing the system to process many more samples with the same human annotation resources, thereby increasing the total number of labeled instances available for model training.
3Productivity
If active learning algorithms are used to select samples for annotation, then annotation efficiency improves, but label quality and quantity become insufficient leading to performance degradation
Solution Approach 1:
The patent changes the selection parameter from traditional active learning criteria (which focus on uncertainty and diversity) to a confidence-based metric that identifies samples where automated models are least certain. By adjusting the confidence threshold parameter, the system can control the balance between selecting enough challenging samples to improve model performance while maintaining efficient annotation throughput. This parameter adjustment ensures that annotation resources are directed to samples that will most effectively improve model reliability without sacrificing annotation efficiency.
Data Source
AI summary
In one embodiment, a method includes determining which objects from a substantial dataset are expected to lead to the largest increase in model quality by applying a samples-selection algorithm using computational capability comprising a processor and/or a memory (e.g., of a processing system and/or a graphics processing unit). The aspect quantifies an informativeness score of data elements in the substantial dataset to determine how likely and/or by what degree data elements will lead to model improvement. The method then automatically determines which data elements of the substantial dataset are in need of human annotation based on a prioritization order derived from the informativeness score and chooses a selected data based on the automatically determining which elements of the substantial dataset are in need of human annotation based on the prioritization order derived from the informativeness score. The method then matches the selected data to an expert.