Hyperparameter tuning involves adjusting settings like learning rate, batch size, or layer depth to optimize performance. This process is documented in " Model development logs, " which record the different " experiments " run by data scientists, the parameters used in each, and the resulting performance metrics (e.g., accuracy or loss). For an auditor, these logs are the " Audit Trail " of the model ' s construction. Data sheets (Option A) provide high-level info on datasets, and explainability reports (Option C) focus on outputs, but development logs provide the technical history of how the model was " tuned " to its final state.