Crowd-Sourced Data Labeling System with Accuracy Thresholds
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Solution Overview
Problem
Traditional data labeling methods relying on experts are expensive, slow, and impractical, while crowd-sourcing, although faster and cheaper, faces challenges such as low accuracy and cheating by workers, making it difficult to achieve high-quality labeled data efficiently.
Innovation Solution
A system that utilizes crowd workers to label data, ensuring high accuracy by having multiple workers review each instance, using an automatic labeler to prevent cheating, and determining the number of responses needed based on an accuracy threshold, thereby reducing unnecessary labeling requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional expert labeling is used, then accuracy is high, but cost and time consumption increase significantly
Solution Approach 1:
The patent segments the labeling task into multiple independent sub-tasks that can be performed by different crowd workers simultaneously. Each worker labels a portion of the data, and the results are aggregated to form the final labeled dataset. This segmentation enables parallel processing while maintaining quality through distributed validation.
Solution Approach 2:
The patent merges multiple labeling results from different crowd workers through a combination process. By aggregating and cross-validating labels from multiple independent workers, the system achieves expert-level accuracy while leveraging the scalability of crowd-sourcing. The merging process includes conflict resolution and consistency checking mechanisms.
2Productivity
If crowd-sourcing is used, then speed and cost efficiency improve, but accuracy and reliability deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where labeling results are validated and verified through multiple rounds of review. Workers receive feedback on their labeling quality, and the system uses feedback loops to identify and correct errors. This feedback-driven approach maintains high accuracy while preserving the speed advantages of crowd-sourcing.
Solution Approach 2:
The patent performs preliminary actions by providing crowd workers with comprehensive guidelines, training materials, and examples before they begin labeling tasks. Quality checklists and validation rules are established in advance to prevent errors rather than correct them later, ensuring high accuracy from the outset while maintaining rapid processing speeds.
3Measurement precision
If more labels are requested to improve accuracy, then overall accuracy increases, but unnecessary expense increases
Solution Approach 1:
The patent applies partial action by requesting labels from a variable number of workers based on the specific requirements of each data instance and the desired confidence level. Rather than uniformly requesting labels from a fixed large number of workers for all instances, the system dynamically adjusts the number of labels requested, obtaining exactly enough labels to achieve the target accuracy without unnecessary excess.
Solution Approach 2:
The patent changes parameters such as the number of workers per task, the complexity of validation rules, and the confidence thresholds based on the characteristics of the data being labeled. This adaptive parameter adjustment optimizes the balance between accuracy and cost, ensuring that resources are allocated efficiently according to the specific needs of each labeling task.
4Measurement precision
If automatic labelers are combined with crowd-sourcing, then accuracy improves, but vulnerability to cheating increases
Solution Approach 1:
The patent introduces an intermediary validation layer between the automatic labeler and the final output. This intermediary process includes consistency checks, cross-validation with crowd worker labels, and anomaly detection mechanisms that prevent cheating by automatic labelers while maintaining the accuracy benefits of automated processing. The intermediary acts as a trusted mediator that verifies the integrity of all labeling sources.
Data Source
AI summary
Systems, methods, and computer-readable storage devices for crowd-sourced data labeling. The system requests a respective response from each of a set of entities. The set of entities includes crowd workers. Next, the system incrementally receives a number of responses from the set of entities until one of an accuracy threshold is reached and m responses are received, wherein the accuracy threshold is based on characteristics of the number of responses. Finally, the system generates an output response based on the number of responses.