The correct answer is D. Using Vertex Explainable AI to generate feature attributions and aggregating these attributions over the entire dataset helps in understanding the rationale behind the model's decisions, much like analyzing the aggregation results with standard model evaluation metrics. This approach provides detailed insights into potential biases and areas of concern by offering feature-level insights, such as pinpointing which image regions contribute most to predictions and revealing systematic biases or patterns of model behavior. This is essential for ensuring transparency and interpretability of the model’s decisions to stakeholders while also identifying any issues in the data that might not be evident through standard evaluation techniques.