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-centered, lower-risk 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
- Explainability- or Fairness-Enhancing Software Packages
- Free Books
- Other Interpretability and Fairness Resources and Lists
- Review and General Papers
- Teaching Resources
- Interpretable ("Whitebox") or Fair Modeling Packages
- Getting a Window into your Black Box Model
- IML
- Interpretable Machine Learning with Python
- Interpreting Machine Learning Models with the iml Package
- Machine Learning Explainability by Kaggle Learn
- Model Interpretability with DALEX
- Model Interpretation series by Dipanjan (DJ) Sarkar:
- Partial Dependence Plots in R
- Visualizing ML Models with LIME
- aequitas
- AI Fairness 360
- anchor
- casme
- cleverhans
- ContrastiveExplanation (Foil Trees)
- deeplift
- deepvis
- eli5
- fairml
- fairness
- Integrated-Gradients
- lofo-importance
- L2X
- lime
- PDPbox
- pyBreakDown
- PyCEbox
- shap
- Skater
- rationale
- tensorflow/lucid
- tensorflow/model-analysis
- Themis
- themis-ml
- treeinterpreter
- woe
- xai
- ALEPlot
- breakDown
- DALEX
- ExplainPrediction
- featureImportance
- forestmodel
- fscaret
- ICEbox
- iml
- lightgbmExplainer
- lime
- live
- mcr
- pdp
- shapleyR
- smbinning
- vip
- xgboostExplainer
- Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models
- Fairness and Machine Learning
- Interpretable Machine Learning
- 8 Principles of Responsible ML
- An Introduction to Machine Learning Interpretability
- Awesome interpretable machine learning ;)
- Awesome machine learning operations
- algoaware
- criticalML
- Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
- Machine Learning Ethics References
- Machine Learning Interpretability Resources
- MIT AI Ethics Reading Group
- XAI Resources
- A Comparative Study of Fairness-Enhancing Interventions in Machine Learning
- A Survey Of Methods For Explaining Black Box Models
- Challenges for Transparency
- Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning
- On the Art and Science of Machine Learning Explanations
- On the Responsibility of Technologists: A Prologue and Primer
- Please Stop Explaining Black Box Models for High-Stakes Decisions
- The Mythos of Model Interpretability
- The Promise and Peril of Human Evaluation for Model Interpretability
- Towards A Rigorous Science of Interpretable Machine Learning
- Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda
- An Introduction to Data Ethics
- Fairness in Machine Learning
- Human-Center Machine Learning
- Practical Model Interpretability