Label Confidence Scoring for Human Data Annotation
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Solution Overview
Problem
Existing data labeling techniques face challenges in accurately assessing the confidence of human-generated labels, which is complex and prone to variability, especially when compared to machine-generated labels, and there is a need for improving labeling quality and efficiency in various applications.
Innovation Solution
The development of techniques for predicting real-time confidence scores for human-generated labels using features such as data characteristics, context, user information, and labeling process details, incorporating machine learning models like ensemble of gradient boosted trees or neural networks to generate label confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human-generated labels are used for data labeling, then labeling flexibility and context understanding are improved, but labeling consistency and accuracy deteriorate due to human variability
Solution Approach 1:
The patent introduces an automated confidence scoring system as an intermediary between human labelers and the final labeled dataset. This system uses machine learning models to evaluate human-generated labels and assign confidence scores, thereby mediating the variability issue while preserving human labeling flexibility. The confidence scores serve as a quantitative measure that bridges subjective human judgment and objective quality assessment.
Solution Approach 2:
The patent implements a feedback mechanism where confidence scores generated by the automated system are used to guide further labeling decisions. High-confidence labels can be accepted automatically, while low-confidence labels trigger review by additional human labelers or re-labeling. This feedback loop continuously improves labeling consistency without eliminating human adaptability.
2Measurement precision
If automated confidence scoring is implemented for human labels, then labeling quality assessment is improved, but system complexity increases
Solution Approach 1:
The patent segments the confidence scoring system into multiple independent components: feature extraction modules that analyze specific aspects of labels (e.g., consistency, completeness), separate machine learning models for different label types, and modular confidence aggregation mechanisms. This segmentation allows each component to be developed and maintained independently, reducing overall system complexity while maintaining measurement precision.
Solution Approach 2:
The system uses self-service mechanisms where the confidence scoring model automatically evaluates its own performance and adjusts its parameters based on feedback from verified labels. This self-calibration reduces the need for manual system configuration and maintenance, offsetting the initial complexity increase with automated self-optimization.
3Reliability
If multiple features are used for confidence prediction, then labeling quality is improved, but computational requirements increase
Solution Approach 1:
The patent implements partial action by selectively applying different sets of features and modeling techniques based on the specific labeling context. For simple, well-defined labeling tasks, only essential features are used. For complex, ambiguous tasks, the full feature set is activated. This approach ensures high labeling quality when needed while minimizing computational energy consumption for routine tasks.
Solution Approach 2:
The system dynamically adjusts the number and type of features used for confidence prediction based on real-time conditions such as label difficulty, user expertise level, and resource availability. The feature selection is not static but adapts to changing requirements, optimizing the balance between labeling quality and computational energy usage.
Data Source
AI summary
Devices and techniques are generally described for confidence score generation for label generation. In some examples, first data may be received from a first computing device. In various further examples, first label data classifying at least one aspect of the first data may be received. First metadata associated with how the first label data was generated may be received. In some cases, the first label data may be generated by a first user. In various examples, a first machine learning model may generate a first confidence score associated with the first label data based at least in part on the first data and second data related to label generation by the first person. In various examples, output data comprising the first confidence score may be sent to the first computing device.