Insufficient Representation in Training Data

When an AI system performs excellently in overall testing, but after deployment its accuracy for a specific group proves markedly lower; due to unmitigated control gaps, investigation reveals that group made up a very small share of the training data, so the model never learned its distinguishing characteristics, leaving that population exposed to a higher risk of misjudgment, triggering compliance exposure and operational reputational costs.