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. The resources in this repository should not be considered legal compliance advice.
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
- acd
- aequitas
- AI Fairness 360
- anchor
- casme
- ContrastiveExplanation (Foil Trees)
- deeplift
- deepvis
- eli5
- fairml
- fairness
- foolbox
- iNNvestigate neural nets
- Integrated-Gradients
- interpret_with_rules
- lofo-importance
- L2X
- lime
- microsoft/interpret
- PDPbox
- pyBreakDown
- PyCEbox
- shap
- Skater
- rationale
- tensorfow/cleverhans
- tensorflow/lucid
- tensorflow/model-analysis
- tensorflow/privacy
- tensorfuzz
- Themis
- themis-ml
- treeinterpreter
- woe
- xai
- ALEPlot
- breakDown
- DALEX
- ExplainPrediction
- featureImportance
- forestmodel
- fscaret
- ICEbox
- iml
- lightgbmExplainer
- lime
- live
- mcr
- pdp
- shapleyR
- shapper
- 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
- ACM FAT* 2019 Youtube Playlist
- An Introduction to Machine Learning Interpretability
- Awesome interpretable machine learning ;)
- Awesome machine learning operations
- algoaware
- criticalML
- Debugging Machine Learning Models (ICLR workshop proceedings)
- Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
- Machine Learning Ethics References
- Machine Learning Interpretability Resources
- MIT AI Ethics Reading Group
- private-ai-resources
- You Created A Machine Learning Application Now Make Sure It's Secure
- XAI Resources
- A Comparative Study of Fairness-Enhancing Interventions in Machine Learning
- A Survey Of Methods For Explaining Black Box Models
- A Marauder’s Map of Security and Privacy in Machine Learning
- Challenges for Transparency
- Explaining Explanations: An Overview of Interpretability of Machine Learning
- Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI
- Interpretable Machine Learning: Definitions, Methods, and Applications
- 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
- Towards A Rigorous Science of Interpretable Machine Learning
- The Security of 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