A data scientist notices that a sentiment analysis model trained on product reviews performs well in testing but poorly on customer service transcripts. Which AI risk concept BEST explains this failure?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because distribution shift — specifically domain shift — occurs when training data (product reviews) and deployment data (customer service transcripts) have different statistical characteristics, causing performance degradation. A is wrong because underfitting manifests as poor training performance, not domain-specific failure.
Full explanation below image
Full Explanation
B is correct because distribution shift — specifically domain shift — occurs when training data (product reviews) and deployment data (customer service transcripts) have different statistical characteristics, causing performance degradation. A is wrong because underfitting manifests as poor training performance, not domain-specific failure. C is wrong because label poisoning is a deliberate attack, not a natural domain mismatch. D is wrong because regularization typically reduces overfitting, and excessive regularization causes underfitting.