An auditor asks which information is automatically captured in an AI Factsheet during model training versus what requires manual entry. Which of the following must be manually entered by a human?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Think of it this way: in real-world AI governance, the business justification narrative explaining why the model was developed is exactly what teams reach for when they need to handle this scenario. AI Factsheets have both automatically populated fields and fields that require human input. On the exam, remember that this falls squarely under the 2.0 AI Lifecycle Governance domain.
Full explanation below image
Full Explanation
AI Factsheets have both automatically populated fields and fields that require human input. Automatic capture covers technical artifacts generated during training: data lineage, evaluation metrics, algorithm details, and hyperparameters are logged programmatically. However, business context fields such as intended use, business justification, and regulatory classification must be manually supplied by the model owner because they represent organizational knowledge that cannot be inferred from the training process alone. Understanding this distinction is critical for ensuring Factsheet completeness. The correct answer, "The business justification narrative explaining why the model was developed", directly addresses the scenario described because it aligns with the specific governance requirement in question. The incorrect options ("Training data source references and data lineage", "Evaluation metrics such as accuracy, AUC, and F1 score", "Model algorithm type and hyperparameter values") may seem plausible but do not satisfy the core requirement. Understanding the distinction between these concepts is critical for IBM watsonx.governance implementations and is frequently tested in the 2.0 AI Lifecycle Governance section of the certification exam.