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Human-in-the-Loop

What is Human-in-the-Loop?

Human-in-the-Loop (HITL) is a process in Edge AI where human feedback or intervention is integrated into machine learning systems to improve decision-making accuracy. Also known as human-assisted AI, it ensures AI models remain reliable and adaptable in real-time, on-device environments.

Why Is It Used?

HITL is used to correct AI errors, provide context-sensitive judgment, and continuously refine models at the edge, where automated systems alone may struggle with complex or ambiguous data.

How Is It Used?

In Edge AI, HITL is applied during training, validation, and deployment phases. Humans label data, verify outputs, and provide feedback to improve AI predictions directly on devices such as IoT sensors, smart cameras, or industrial machinery.

Types of Human-in-the-Loop

  • Active HITL: Humans intervene during live AI decision-making.

  • Passive HITL: Human feedback is collected post-decision for future model training.

  • Hybrid HITL: Combines real-time and post-process human oversight for optimal accuracy.

Benefits of Human-in-the-Loop

  • Improves AI accuracy in complex, real-world scenarios.

  • Enables continuous learning and model adaptation at the edge.

  • Reduces risk of automated errors in critical applications.

  • Enhances trust and accountability in AI-driven systems.

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