Operationalizing Trustworthy Decision Intelligence

This unit focuses on making decision intelligence systems reliable, ethical, and actionable in business contexts. Students will explore human-in-the-loop design, data lineage and model drift, bias and trustworthiness, tool selection (including AutoML), and the organizational roles and processes needed to operationalize decisions — building on foundational concepts and preparing learners to implement and govern DI solutions in subsequent units.

Learning Objectives

• Analyze sources of data and model degradation (including model drift and lineage gaps) and propose prioritized mitigation strategies with measurable success criteria
• Evaluate AI/ML outputs and decision recommendations for bias, fairness, context-sensitivity, and business fit using structured rubrics and case evidence
• Apply decision intelligence frameworks to design human-in-the-loop workflows, escalation rules, and limited-action plans that align with an organization’s capacity and ethical constraints
• Demonstrate selection and governance of DI tools (including AutoML), document end-to-end DI workflows and roles, and produce a short implementation roadmap that addresses trust, accountability, and operationalization