Image Label Grouping to Detect AI-Human Label Mismatches
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Solution Overview
Problem
In supervised learning for image classification, manual labeling is costly and prone to inconsistencies due to variations in operator determination, making it difficult to maintain label quality across a large number of images.
Innovation Solution
A data labeling work support apparatus that includes a processor to acquire and group images based on feature similarity, assign AI labels, calculate matching degrees between AI and manual labels, and output information on label inconsistencies, reducing the need for manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling is performed by multiple operators to ensure label quality, then label accuracy may be maintained through human review, but the workload and time required to check labels increases significantly
Solution Approach 1:
An AI model serves as an intermediary to automatically assign labels to images, reducing the need for manual labeling and review. The system compares AI-assigned labels with human-assigned labels to identify inconsistencies, allowing administrators to focus only on problematic cases rather than reviewing all labels manually.
Solution Approach 2:
The patent replaces the mechanical process of manual label review with an automated system that uses AI models to assign labels and algorithms to detect inconsistencies. This substitution dramatically reduces the time and human resources required for label verification while maintaining quality through automated anomaly detection.
2Productivity
If AI labeling is used to reduce manual work, then productivity increases, but label consistency may deteriorate due to variations in operator determination
Solution Approach 1:
The system implements feedback by comparing AI-assigned labels with human-assigned labels and identifying inconsistencies. This feedback mechanism allows the system to learn from discrepancies and improve future labeling accuracy, while also providing administrators with information about potential errors to correct.
Solution Approach 2:
The patent merges AI labeling capabilities with human labeling expertise by combining both approaches. The system uses AI to handle high-volume labeling efficiently while incorporating human judgment to verify and correct labels, particularly for complex or ambiguous cases, thereby achieving both speed and consistency.
3Reliability
If administrators review all labels manually to ensure quality, then label consistency is maintained, but the complexity and cost of the labeling process increases
Solution Approach 1:
The labeling process is segmented into different stages: AI performs initial labeling, automated systems perform consistency checks, and human administrators review only identified inconsistencies. This segmentation reduces the complexity of manual review by dividing the workload across multiple systems and human resources with specific, focused tasks.
Data Source
AI summary
According to one embodiment, a data labeling work support apparatus includes a processor including hardware. The processor acquires a first label assigned to data. The processor acquires the data. The processor extracts a feature of the data. The processor groups the data based on a similarity or a distance of the feature. The processor assigns a second label to the grouped data. The processor calculates a degree of matching between the first label and the second label. The processor outputs information regarding a combination of the first label and the second label having a low degree of matching.