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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

Associative Learning

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Related Experiment Videos

Beyond uncertainty in modern active learning for trustworthy AI.

Ridha Horchani¹

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.

Keywords:

active learning annotation cost data-efficient learning evaluation realism human-in-the-loop machine learning supervision allocation trustworthy AI

Related Experiment Videos

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.