abhishekms1047 / awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

awesome-machine-learning-interpretability

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.

Table of Contents

• Comprehensive Software Examples and Tutorials
• Interpretability and Fairness Software Packages
  • Python
  • R
• Free Books
• Other Interpretability and Fairness Lists
• Review Papers
• Teaching Resources
• Whitebox Modeling Packages
  • C/C++
  • Python
  • R

Comprehensive Software Examples and Tutorials

• 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

Interpretability and Fairness Software Packages

Browser

• What-if Tool

Python

• 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

R

• ALEPlot
• breakDown
• DALEX
• ExplainPrediction
• ICEbox
• iml
• lightgbmExplainer
• lime
• live
• pdp
• shapleyR
• vip
• xgboostExplainer

Free Books

• 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

Other Interpretability and Fairness Lists

• criticalML
• Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
• Machine Learning Ethics References
• Machine Learning Interpretability Resources
• XAI Resources

Review and General Papers

• 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

Teaching Resources

• An Introduction to Data Ethics
• Fairness in Machine Learning
• Practical Model Interpretability

Whitebox Modeling Packages

C/C++

• Certifiably Optimal RulE ListS

Python

• Bayesian Case Model
• Bayesian Ors-Of-Ands
• Bayesian Rule List (BRL)
• fair-classification
• Falling Rule List (FRL)
• H2O-3
  • Penalized Generalized Linear Models
  • Sparse Principal Components (GLRM)
• Monotonic XGBoost
• pyGAM
• Risk-SLIM
• sklearn-expertsys
• Scikit-learn
  • Decision Trees
  • Generalized Linear Models
  • Sparse Principal Components
• Super-sparse Linear Integer models (SLIMs)
• woe

R

• arules
• Causal SVM
• elasticnet
• gam
• glmnet
• H2O-3
  • Penalized Generalized Linear Models
  • Sparse Principal Components (GLRM)
• Monotonic XGBoost
• quantreg
• rpart
• RuleFit
• Scalable Bayesian Rule Lists (SBRL)
• smbinning