Interpretable models

• scikit-learn
• nearest-neighbors - learn.org/stable/modules/linear_model.html#lasso), [decision trees](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html), and more
• glmnet

Post-hoc explanations

• LIME (Local Interpretable Model Explanations) method
• SHAP (SHapley Additive exPlanations) method
• scoped rules/anchors - Precision Model-Agnostic Explanations](https://homes.cs.washington.edu/~marcotcr/aaai18.pdf)
• Alibi
• marcotcr/lime
• slundberg/shap
• marcotcr/anchor

• pair-code/what-if-tool
• EthicalML/xai
• Quantus

Post-hoc explanations

• Interpretable AI
• Interpretable Machine Learning

Post-hoc explanations

• CS335: Fair, Accountable, and Transparent (FAccT) Deep Learning
• Slides and recorded lectures

Post-hoc explanations

• Towards A Rigorous Science of Interpretable Machine Learning (2017) - Velez and Kim
• "Why Should I Trust You?": Explaining the Predictions of Any Classifier (2016)
• Visualizing and Understanding Convolutional Networks (2014)
• Sanity Checks for Saliency Maps (2018)
• Counterfactual Explanations Without Opening The Black Box: Automated Decisions and the GDPR (2017)
• Explain Yourself - A Primer on ML Interpretability & Explainability (2021)

Post-hoc explanations

• Poly-cam high resolution class activation map for convolutional neural networks (2022)