A predictive maintenance AI system at a manufacturing facility fails to flag an imminent equipment failure that caused a production shutdown. Post-incident analysis finds the failure mode was underrepresented in training data. How should this risk be classified?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — a is correct because the model's failure to detect a rare but catastrophic failure mode reflects tail risk — the training data distribution underrepresents low-frequency, high-severity events, causing the model to be blind to them. B is the opposite — overfitting to common patterns would still cause the model to miss rare events, but the root cause classification is tail risk from data gaps.
Full Explanation
A is correct because the model's failure to detect a rare but catastrophic failure mode reflects tail risk — the training data distribution underrepresents low-frequency, high-severity events, causing the model to be blind to them. B is the opposite — overfitting to common patterns would still cause the model to miss rare events, but the root cause classification is tail risk from data gaps. C requires evidence of deliberate manipulation. D requires evidence of a change in the underlying process.