A curated, but probably biased and incomplete, 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.
An incomplete, imperfect blueprint for a more human-friendly machine learning. The resources in this repository can be used to do many of these things today.
Image credit: H2O.ai Machine Learning Interpretability team, https://github.com/h2oai/mli-resources.
- Comprehensive Software Examples and Tutorials
- Interpretability and Fairness Software Packages
- Free Books
- Other Interpretability and Fairness Lists
- Review Papers
- Teaching Resources
- Whitebox Modeling Packages
- Getting a Window into your Black Box Model
- Hands-on Machine Learning Model Interpretation
- IML
- Interpretable Machine Learning with Python
- Interpreting Machine Learning Models with the iml Package
- Model Interpretability with DALEX
- Visualizing ML Models with LIME
- aequitas
- AI Fairness 360
- anchor
- cleverhans
- ContrastiveExplanation (Foil Trees)
- eli5
- fairml
- L2X
- lime
- PDPbox
- pyBreakDown
- PyCEbox
- shap
- Skater
- tensorflow/lucid
- tensorflow/model-analysis
- Themis
- themis-ml
- treeinterpreter
- ALEPlot
- breakDown
- DALEX
- ExplainPrediction
- ICEbox
- iml
- lightgbmExplainer
- lime
- live
- pdp
- shapleyR
- vip
- xgboostExplainer
- An Introduction to Machine Learning Interpretability
- Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models
- Fairness and Machine Learning
- Interpretable Machine Learning
- criticalML
- Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
- Machine Learning Ethics References
- Machine Learning Interpretability Resources
- XAI Resources
- A Survey Of Methods For Explaining Black Box Models
- Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning
- On the Art and Science of Machine Learning Explanations
- 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