human in the loop
AI, ML & GenAI interview questions tagged human in the loop, across every topic.
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Concepts behind "human in the loop"
The curriculum that explains the ideas these questions test.
Core
Automation Bias and Effective Human OversightAutomation bias is the documented tendency for a person shown a confident machine recommendation to anchor on it and stop hunting for contradicting evidence, which makes 'human in the loop' weakest exactly where the model is wrong. Effective oversight means designing against that: independent-then-reveal review, calibrated uncertainty instead of a single confident label, blind audits that measure the real override rate, and evaluating the human-plus-model pair rather than the model alone. AI, ML, and GenAI interviews probe it because almost every risk register lists a human reviewer as the mitigation and almost nobody measures whether that reviewer changes any outcome.🛡️ AI Security, Privacy & GovernanceSign in
Advanced
Agent Reliability and Long-Horizon RobustnessAgents over long horizons break down because per-step reliability multiplies: a step that works 95 percent of the time drops to roughly 60 percent across ten steps. The discipline spans consistent completion (not pass@k), recovering from errors, step and token budgets, human-in-the-loop checkpoints, and stopping cascading failure inside multi-agent systems. AI, ML, and GenAI engineer interviews test this to tell apart people who built a demo from people who shipped an agent that survives thousands of runs.🤖 Retrieval & Agents🔒 Premium