Entropy· 2026Q2· Review
Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications
- 43citations
- Q2SCImago
- 2026year
Short summary
A systematic review proposes a unified taxonomy for Human-in-the-Loop (HITL) AI systems, categorizing them by loop placement, interaction granularity, and temporal characteristics.
AI-generated from the title and abstract; the full text is not read.
Key points
- HITL AI combines machine learning with human oversight, feedback, and decision-making.
- A unified taxonomy categorizes HITL systems by loop placement, interaction granularity, and temporal characteristics.
- The review covers theoretical foundations, technical methods, ethical considerations, and applications in high-risk domains.
- Challenges include scalability, cognitive load, and trust calibration in practical HITL deployments.
AI-generated from the title and abstract; the full text is not read.
Abstract
The integration of human judgment into artificial intelligence (AI) systems has emerged as a key research direction, particularly for high-stakes applications where full automation remains insufficient. Human-in-the-Loop (HITL) AI represents a field that combines machine learning capabilities with human oversight, feedback, and decision-making at various stages of the AI pipeline. This survey provides a systematic review of HITL approaches, covering theoretical foundations, technical methods, ethical considerations, and domain-specific applications. We propose a unified taxonomy that categorizes HITL systems based on loop placement, interaction granularity, and temporal characteristics. This review synthesizes findings from healthcare, autonomous systems, cybersecurity, and other high-risk domains where human oversight is essential. We also examine the challenges of scalability, cognitive load, and trust calibration that affect the practical deployment of HITL systems. The final section outlines open research directions and introduces a framework for designing effective human-AI collaborative systems.
The authors' abstract, as published at the source. Entropy, 2026 · DOI ↗
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Field: Social Psychology
Social PsychologyPsychology