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Human-in-the-loop AI
Definition
Human-in-the-loop AI refers to AI systems designed to incorporate human judgment, review, or intervention at defined points in the system's decision-making or execution process.
Purpose
The purpose of human-in-the-loop AI is to improve reliability, safety, accountability, and decision quality by combining automated model behavior with human oversight where full autonomy is undesirable or unsafe.
Key Characteristics
- Explicit points where human review, approval, or intervention is required
- Combination of automated decision-making with manual validation or correction
- Ability to pause, override, or modify AI-generated actions or outputs
- Use of human feedback to correct errors or guide system behavior
- Integration with monitoring, auditability, and access control mechanisms
Usage in Practice
In practice, human-in-the-loop AI is used in regulated, high-risk, or high-impact domains to review model outputs, approve actions, handle edge cases, and mitigate failure modes that cannot be reliably addressed through automation alone.
One implementation of this concept is offered by Kenaz through the AI Safety & Compliance Audit service.