Trustworthy Models and Human-in-the-Loop

This unit examines how to make decision intelligence (DI) systems reliable, ethical, and operationally useful by focusing on model trust, drift, bias, data lineage, and human-in-the-loop designs. It builds on foundational DI concepts and prepares learners to design governance, tooling, and operational workflows in subsequent units.

Learning Objectives

• Analyze sources of model error, drift, and trust breakdowns by reviewing case studies and diagnostic metrics to identify at least three distinct failure modes and their root causes
• Apply data lineage and governance techniques to document dataset provenance, feature/version history, and model versions for a sample DI pipeline, producing a clear lineage map
• Evaluate ethical and equity risks (including algorithmic bias and racist algorithms) in DI solutions and propose at least two mitigations with documented trade-offs
• Demonstrate design of a human-in-the-loop decision workflow that selects appropriate DI tools (including when to limit AutoML) and constrains recommendations to actions the organization can feasibly execute, including monitoring and escalation triggers