AIGP Question of the Day — 16 August 2026
A new free AIGP practice question every few days, with a full explanation.
During evaluation, reviewers consistently accept the model's recommendations even when case files contain contradicting evidence, because 'the system is usually right'. Which risk does this behaviour create for the governance programme?
- Data poisoning of the training corpus
- Automation bias undermining the human oversight control
- Model drift caused by reviewer feedback
- Overfitting to reviewer preferences
Over-trusting automated output is automation bias, and it hollows out human oversight: the control exists on paper but no longer functions (b). No attacker is altering training data (a), no distribution change is described (c), and overfitting (d) is a training phenomenon, not reviewer behaviour.
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