Trustworthy Decision Intelligence Practices

This unit deepens students’ ability to evaluate and operationalize decision intelligence (DI) in real organizations, emphasizing trust, governance, and human-centered judgment. Students will learn to detect model issues, trace data lineage, mitigate bias, and design implementable DI plans that respect business constraints and ethical considerations.

Learning Objectives

• Analyze how human judgment, upstream/downstream workflows, and organizational constraints influence DI outcomes by mapping decision workflows and identifying potential failure points.
• Evaluate AI and machine learning models for reliability, bias, and model drift by applying diagnostic techniques, documenting evidence, and proposing mitigation strategies.
• Apply data governance and provenance practices by tracing data lineage, assessing data quality, and specifying controls to reduce misuse and biased outputs.
• Demonstrate synthesis of tools, roles, and implementation limits by recommending an actionable DI plan that aligns models, operational constraints, and organizational responsibilities (e.g., CDO, AutoML controls).