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 5 from Interpretable Machine Learning book by Christoph Molnar

Suggested Readings:

• LIME, SIGKDD, 2016
• SHAP, NeurIPS 2017

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:

• Visualising image classification models and saliency maps (ICLRW-2012)
• Visualizing and understanding CNNs (ECCV-2013)
• SmoothGrad (2017)
• Interpreting DNNs is fragile (AAAI-2019)
• Sanity Checks for Saliency Maps (NeurIPS-2018)
• The (un) reliability of saliency methods (Explainable AI-2019)
• Sanity Checks for Saliency Metrics (AAAI-2020)

Suggested Readings:

Suggested Readings: