A model risk management team adds a fairness assessment requirement: models must demonstrate that precision does not differ by more than 5 percentage points across demographic groups. Which SAS Model Manager or SAS Viya workflow step supports this group-stratified fairness metric computation?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because SAS does not have a single automatic fairness testing button; the standard approach is to segment the model assessment by demographic group (e.g., using PROC ASSESS with a BY GROUP= or custom scoring code), compute precision per group, compare the difference, and document/store findings in the SAS Model Manager model record as custom properties or KPIs. A (aggregate metrics) hides group differences.
Full explanation below image
Full Explanation
B is correct because SAS does not have a single automatic fairness testing button; the standard approach is to segment the model assessment by demographic group (e.g., using PROC ASSESS with a BY GROUP= or custom scoring code), compute precision per group, compare the difference, and document/store findings in the SAS Model Manager model record as custom properties or KPIs. A (aggregate metrics) hides group differences. C is wrong; SAS Visual Analytics does not auto-compute fairness metrics from model input schemas. D is wrong; PROC HPFOREST has no fairness GROUP= statement.