Course Objectives:  The main objectives of the course are to 

● Understand the importance of explainability in AI and its impact on stakeholders. 

● Explore different techniques and methods for making AI systems explainable. 

● Analyze the trade-offs between model complexity and interpretability. 

● Examine the ethical and societal implications of XAI. 

● Apply XAI techniques to real-world datasets and scenarios. 

UNIT I:  Introduction to Explainable AI (XAI): Motivations for XAI, Importance of interpretability and transparency Techniques for XAI, Model-specific interpretability methods (e.g., decision trees, rulebased systems) Model-agnostic interpretability methods (e.g., LIME,SHAP) Post-hoc explanation techniques (e.g., feature importance, counterfactual explanation.

UNIT II: Interpretable Models: Linear models, Decision trees and rule-based systems Symbolic AI approaches, Interpretable Neural Networks, Sparse neural networks, Attention mechanisms, Layer-wise relevance propagation (LRP) 

UNIT III: Methods for Explainable AI: Partial Dependence Plot (PDP), Conformal Prediction, Individual Conditional Expectation (ICE), Feature Importance, Saliency Maps, Local Interpretable Model-Agnostic Explanations (LIME), SHAP, Integrated Gradient (IG), Explainability for Linear Models, Non-linear models and Deep Learning Models. 

UNIT IV: Evaluation of XAI Methods: Quantitative metrics for interpretability, Human centric evaluation methods, Ethical and Societal Implications of XAIB, is and fairness in interpretable AI, Trust and accountability in AI systems, Regulatory considerations.

UNIT V: Applications of XAI: Healthcare (e.g., medical diagnosis, personalized treatment) Finance (e.g., credit scoring, fraud detection), Autonomous systems (e.g., self-driving cars, drones). Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision 

Text Books: 

1. "Interpretable Machine Learning" by Christoph Molnar 

2. "Explainable AI: Interpreting, Explaining and Visualizing Deep Learning" by L. Liu and G. Hu 

Reference Books: 

1. "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable" by Christoph Molnar 

2. "Explainable AI: Interpreting, Explaining and Visualizing Deep Learning" by L. Liu and G. Hu – 

3. "Explainable AI in Healthcare: Exploring Interpretable Models and Learning from Patient Data" edited by F. E. Elsayed and B. G. Stoecklin 

Online Resources: 

1. https://christophm.github.io/interpretable-ml-book/