Program Objectives:

• To promote transparent and responsible AI in critical sectors

• To bridge the gap between model performance and stakeholder trust

• To enable organizations to meet legal and ethical standards

• To integrate explainability as a core part of AI system development

What you will learn?

Week 1: Foundations of Explainable AI
Module 1: Understanding Explainability in AI

• Chapter 1.1: What is Explainable AI?

• Chapter 1.2: Why Explainability Matters in Industry

• Chapter 1.3: Regulatory and Ethical Drivers (GDPR, AI Act)

Module 2: XAI Methods and Taxonomy

• Chapter 2.1: Model-Specific vs. Model-Agnostic Techniques

• Chapter 2.2: Global vs. Local Explanations

• Chapter 2.3: Common Techniques: SHAP, LIME, Anchors, Partial Dependence

Week 2: Tools and Techniques in Practice
Module 3: XAI in Machine Learning Pipelines

• Chapter 3.1: Integrating SHAP in Tree-based Models

• Chapter 3.2: Using LIME for Text and Image Classification

• Chapter 3.3: Visualizing Feature Importance and Decision Boundaries

• Chapter 3.4: Performance vs. Interpretability Trade-Offs

Module 4: Toolkits for Explainability

• Chapter 4.1: Overview of XAI Libraries (SHAP, LIME, ELI5, Captum)

• Chapter 4.2: Explainability in Deep Learning with Captum

• Chapter 4.3: Explainability in Production with MLflow and dashboards

• Chapter 4.4: Case: Interpreting Drift and Model Updates in Production

Week 3: Real-World Deployment and Use Cases
Module 5: Industry Applications of XAI

• Chapter 5.1: Financial Services – Credit Risk, Fraud Detection

• Chapter 5.2: Healthcare – Diagnostics, Clinical Decision Support

• Chapter 5.3: Manufacturing – Predictive Maintenance, QA Automation

Module 6: Auditing, Compliance, and Communication

• Chapter 6.1: Model Auditing and Documentation Practices

• Chapter 6.2: Explaining AI to Non-Technical Stakeholders

• Chapter 6.3: Building Trustworthy AI Pipelines

• Chapter 6.4: Capstone – Develop an XAI Dashboard for Business Decision Makers