Definition
Human-in-the-loop (HITL) refers to AI systems that require human intervention at various stages of the workflow, such as data labeling, model evaluation, or final decision approval. This approach ensures accountability, improves model accuracy through feedback, and mitigates risks associated with fully autonomous systems. It is particularly critical in high-stakes domains like healthcare and finance, where human judgment is necessary to validate AI outputs and handle edge cases that automated systems may misinterpret.
Summary
A system design where humans actively participate in the decision-making or feedback process of an AI model.
Key Concepts
- Feedback Loop
- Accountability
- Validation
- Supervision
Use Cases
- Medical diagnosis verification
- Content moderation review
- Financial fraud detection approval