Which evaluation metric categories are essential for CPMAI model assessment?

• A. Accuracy/precision/recall, calibration, fairness/bias metrics, robustness to drift, reliability, and interpretability
• B. Only accuracy
• C. Throughput and latency only
• D. Cost and energy efficiency

Answer: (D)

Evaluating CPMAI models requires a balanced view of performance, trust, and stability. You look beyond just whether predictions are correct and consider a suite of metrics that together show how well the model operates in real-world, diverse settings. Accuracy and related measures (such as precision and recall) capture how often the model is correct and how it handles different types of errors. Calibration ensures the model’s confidence scores align with actual frequencies, so a 70% prediction truly reflects a real 70% likelihood. Fairness and bias metrics examine whether the model treats different groups equitably, which is essential for responsible AI. Robustness to drift checks how performance holds up when data distributions shift over time, helping ensure stability in changing environments. Reliability looks at consistency across runs and datasets, and interpretability focuses on making the model’s decisions understandable to humans. While cost and energy efficiency matter for deployment and operational considerations, they do not measure the model’s predictive quality, fairness, or reliability themselves.