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Beyond uncertainty in modern active learning for trustworthy AI
1Department of Physics, College of Science, Sultan Qaboos University, Muscat, Oman.
Frontiers in Artificial Intelligence
|July 6, 2026
Summary
Active learning (AL) needs better strategies for selecting data to label, moving beyond simple sample selection to robust supervision allocation pipelines. This ensures reliable gains in real-world AI applications.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Data Science
Background:
- Active learning (AL) addresses the challenge of expensive data labeling in AI.
- Current AL lacks unified principles, leading to fragmented research and unreliable comparisons.
Purpose of the Study:
- To critically review and synthesize modern AL strategies.
- To propose a taxonomy and research agenda for trustworthy AL.
Main Methods:
- A four-axis taxonomy: acquisition logic, supervision granularity, operational regime, and evaluation realism.
- Comparison of major AL acquisition families (uncertainty, disagreement, etc.).
Main Results:
- Identified fragmentation in AL research and evaluation protocols.
- Highlighted trade-offs, strengths, and failure modes of various acquisition strategies.
- Distilled design principles for robust and trustworthy AL systems.
Conclusions:
- The primary challenge in AL has shifted from sample selection to designing reliable supervision-allocation pipelines.
- Emphasis on annotation cost, robustness, fairness, and workflow-grounded evaluation is crucial for practical AL deployment.
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