Human-in-the-Loop
A system design where humans are integrated into the AI workflow to provide oversight, make decisions, correct errors, or handle edge cases that the AI cannot reliably manage alone.
Why It Matters
HITL is essential for high-stakes AI — it maintains accuracy and accountability while still leveraging AI for speed and scale.
Example
A content moderation system where AI flags potentially harmful posts, but human reviewers make the final decision on borderline cases.
Think of it like...
Like autopilot on an airplane — the AI handles routine flying, but the human pilot takes over for takeoff, landing, and anything unusual.
Related Terms
Active Learning
A training strategy where the model identifies the most informative unlabeled examples and requests human labels only for those. This minimizes labeling effort by focusing on the examples that matter most.
AI Safety
The research field focused on ensuring AI systems operate reliably, predictably, and without causing unintended harm. It spans from technical robustness to long-term existential risk concerns.
Guardrails
Safety mechanisms and constraints built into AI systems to prevent harmful, inappropriate, or off-topic outputs. Guardrails can operate at the prompt, model, or output level.
Responsible AI
An approach to developing and deploying AI that prioritizes ethical considerations, fairness, transparency, accountability, and societal benefit throughout the entire AI lifecycle.