Schedule

Suggested Readings:

• Ch-3.1 from Interpretable Machine Learning book by Christoph Molnar
• Towards A Rigorous Science of Interpretable Machine Learning, Doshi-Velez and Kim, 2017
• Interpretable machine learning: definitions, methods, and applications, Murdoch et al. 2019
• Explanation in artificial intelligence: Insights from the social sciences, Tim Miller, 2019
• Examples are not Enough, Learn to Criticize! Criticism for Interpretability, Kim et al. 2016

Suggested Readings:

• Ch 3.2 to 3.6 from Interpretable Machine Learning book by Christoph Molnar

Suggested Readings:

• Ch 3.1, 4.1 to 4.3 from Introduction to Statistical Learning
• Ch 3.2 to 3.6 from Interpretable Machine Learning book by Christoph Molnar

Suggested Readings:

• Ch 5.3 to 5.7 from Interpretable Machine Learning book by Christoph Molnar

Suggested Readings:

• Ch 6, Ch 7, Ch 8.1 and 8.2 from Interpretable Machine Learning book by Christoph Molnar

Suggested Readings:

• Ch 8.3 and 8.7 from Interpretable Machine Learning book by Christoph Molnar

Suggested Readings:

• LIME, SIGKDD, 2016

Suggested Readings:

• Counterfactual Explanations without Opening the Black Box (Harvard Journal of Law & Technology, 2018)
• Multi-Objective Counterfactual Explanations (PPSN, 2020)
• Counterfactuals and Causability in XAI

Suggested Readings:

• Distil on Visualization

Suggested Readings:

Suggested Readings:

Suggested Readings:

• Primer on Causal Inference by Judea pearl et al.
• The Book of Why by Judea Pearl and Dana Mackenzie
• Handbook on Causality
• Causality101.net
• Survey on XAI and Causality by Gianluca Carloni et al., September 2023
• Surevy on XMl and Causality by Moraffah et al. KDD 2020

Suggested Readings:

Suggested Readings:

Suggested Readings:

Suggested Readings: