Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems

📰 ArXiv cs.AI

A novel framework for efficient human-in-the-loop active learning is proposed for data labeling in AI systems

advanced Published 31 Mar 2026
Action Steps
  1. Identify the most informative samples for labeling using active learning algorithms
  2. Select the optimal data points to be labeled by human experts
  3. Implement a human-in-the-loop feedback mechanism to improve the model's performance
  4. Continuously evaluate and refine the active learning strategy to minimize labeling costs
Who Needs to Know This

Data scientists and AI engineers on a team benefit from this framework as it optimizes the use of expert time for labeling data, while product managers can utilize the efficiently labeled data to improve AI model performance

Key Insight

💡 Human-in-the-loop active learning can significantly reduce the cost and time required for labeling data in AI systems

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🤖💡 Efficient human-in-the-loop active learning for AI data labeling

Key Takeaways

A novel framework for efficient human-in-the-loop active learning is proposed for data labeling in AI systems

Full Article

Title: Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems

Abstract:
arXiv:2501.00277v2 Announce Type: replace-cross Abstract: Modern AI algorithms require labeled data. In real world, majority of data are unlabeled. Labeling the data are costly. this is particularly true for some areas requiring special skills, such as reading radiology images by physicians. To most efficiently use expert's time for the data labeling, one promising approach is human-in-the-loop active learning algorithm. In this work, we propose a novel active learning framework with significant
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