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Human in the Loop (HITL)

Definition
AI-generated

Human in the loop (HITL) is a design pattern where people review, correct, approve, or label model or agent outputs before they commit actions—combining automation with explicit checkpoints, escalation rules, and audit trails rather than fully end-to-end autonomy.

Topics
LLM and Agents
Synonyms
Human-in-the-loop HITL
Related
AI Agent Genome-Wide Association Study (GWAS) Harness

Why it matters in GWAS

Curation, phenotype harmonization, and target nomination touch regulated and irreversible decisions; HITL workflows (e.g. analyst sign-off on allele fixes, variant lists, or public text) reduce the risk that LLM or pipeline errors propagate into meta-analyses or participant-facing materials.

Example usage

"The LLM drafted ICD code mappings, but release to the meta-analysis required human-in-the-loop approval on every changed cohort field."

AI Agent Genome-Wide Association Study (GWAS) Harness Harness Engineering Large Language Model (LLM) Reinforcement Learning from Human Feedback (RLHF)
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Amershi S, et al. (2019). Guidelines for human-AI interaction. CHI.
  • Holzinger A. (2016). Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inform.
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