Representativeness ensures that the training data reflects the full spectrum of conditions the AI model will encounter in production. According to the AAIA™ Study Guide, models trained on non-representative data are prone to bias, poor generalization, and underperformance in real-world applications.
"Ensuring that training data accurately represents the operational environment is critical for model reliability, fairness, and scalability. Without it, the model may perform well in testing but fail in actual usage." Timeliness (A) and understandability (D) support performance and usability, but they are secondary to ensuring data coverage. Predictability (B) may not be desirable in dynamic modeling.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Training Data Characteristics and Model Validity"