A data science team trains a classification model that predicts loan approval outcomes.

A loan approval model must be assessed for both transparency and demographic fairness before registration. Option D (feature importance) addresses prediction transparency — using SHAP or Azure ML ' s Responsible AI dashboard, the team can see which features drive individual predictions, revealing whether proxy variables for protected attributes are influencing decisions inappropriately. Option E (error rates across demographic cohorts) directly measures whether the model makes more mistakes for specific groups — a core fairness metric required under Responsible AI principles. Option A (global error rates) masks disparities since you need cohort-level analysis. Option B (endpoint latency) is a performance engineering concern. Option C (inference schema compatibility) is a technical integration check. The Azure ML Responsible AI dashboard combines both error analysis and feature importance in a unified interface designed for exactly this type of pre-deployment fairness assessment.

Microsoft Learn Reference Topic: Responsible AI dashboard in Azure Machine Learning – Error analysis and model interpretability