AI Glossary · Governance & Ops
Human-in-the-Loop (HITL)
A design pattern where humans review, approve or correct AI decisions — essential for high-risk or low-confidence tasks.
Definition
What is Human-in-the-Loop (HITL)?
Human-in-the-Loop (HITL) is a design pattern where humans review, approve or correct AI decisions — essential for high-risk or low-confidence tasks.
- Category
- Governance & Ops
- Glossary set
- 20 related terms
- Audience
- Enterprise AI leaders
Why does Human-in-the-Loop (HITL) matter in enterprise AI?
Human-in-the-Loop (HITL) matters in AI operations because it affects risk controls, monitoring, accountability, and the evidence enterprise teams need before scaling.
Related terms in Governance & Ops
- AIOps
- Applying machine learning to IT operations data (logs, metrics, traces) to detect anomalies, correlate incidents and automate remediation.
- AI Gateway
- A managed proxy that routes model calls, enforces quotas, redacts PII, logs prompts, and applies policy across multiple LLM providers.
- Bias (Model Bias)
- Systematic error in model output that disadvantages a group or skews decisions — introduced through data, labeling, objectives or deployment context.
- Data Loss Prevention (DLP)
- Detecting and blocking sensitive data (PII, PCI, PHI, IP) from leaving controlled systems — a critical layer around LLM inputs and outputs.
- Deepfake
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- Drift (Model Drift)
- Degradation in model quality over time as input data or the world changes; detected through monitoring and addressed with retraining or prompt updates.