A curated list of awesome machine learning interpretability resources.
If you want to contribute to this list (and please do!) read over the contribution guidelines, send a pull request, or contact me @jpatrickhall.
- Comprehensive Software Examples and Tutorials
- Interpretability and Fairness Software Packages
- Free Books
- Other Interpretability and Fairness Lists
- Review Papers
- Whitebox Modeling Packages
- aequitas
- anchor
- eli5
- fairml
- lime
- PDPbox
- PyCEbox
- shap
- Skater
- tensorflow/model-analysis
- themis-ml
- treeinterpreter
- An Introduction to Machine Learning Interpretability
- Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models
- Interpretable Machine Learning
- criticalML
- Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
- Machine Learning Ethics References
- Machine Learning Interpretability Resources
- A Survey Of Methods For Explaining Black Box Models
- Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning
- The Mythos of Model Interpretability
- Towards A Rigorous Science of Interpretable Machine Learning
- Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda
- fair-classification
- H2O-3
- Monotonic XGBoost
- pyGAM
- sklearn-expertsys
- Scikit-learn