Program Structure

Module 1: Why Explainable AI?

• Trust, safety, and compliance in AI systems.
• When explanations are required: high-stakes decisions.
• What explanations can/can’t prove.
• XAI workflow: model → explain → validate → report.

Module 2: Interpretable Models First

• Linear/logistic regression interpretation.
• Decision trees and rule-based models.
• Monotonic constraints (intro) and simple models that work well.
• Choosing interpretable baselines before complex models.

Module 3: Global Explainability

• Permutation importance and gain-based importance.
• Partial dependence and ICE plots (conceptual + practice).
• Interaction effects (intro).
• Stability of explanations across folds/samples.

Module 4: Local Explainability (Instance-Level)

• SHAP basics: additive explanations and common plots.
• LIME basics: local surrogate explanations (intro).
• Case-based explanations: similar examples (overview).
• When local explanations mislead and how to reduce risk.

Module 5: Explaining Text and Images (Intro)

• NLP explanations: token importance, perturbation concepts.
• Vision explanations: saliency/Grad-CAM concept (overview).
• Choosing explanation methods based on task.
• Limitations and failure modes.

Module 6: Debugging Models with XAI

• Spot leakage and shortcut learning.
• Detect spurious features and data artifacts.
• Bias checks: group metrics and error breakdown.
• Counterfactual thinking: “what needs to change?” (intro).

Module 7: Governance, Monitoring & Documentation

• Model cards: purpose, data, metrics, limits.
• Data drift and concept drift monitoring (overview).
• Human review loops for high-risk use cases.
• Audit-ready documentation checklist.

Module 8: Capstone Build Sprint

• Pick a model: churn, credit risk (non-regulated demo), healthcare (non-clinical), HR, demand.
• Run explainability + bias checks + error analysis.
• Create a final report with visuals and recommendations.
• Prepare a short presentation for stakeholders.

Final Project

• Deliverables: model + explanation notebook + dashboard/plots + audit report.
• Include: risks, limits, monitoring plan, and decision guidance.