Automation Bias and Effective Human Oversight

Automation 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.