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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-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.

Table of Contents

• Comprehensive Software Examples and Tutorials
• Explainability- or Fairness-Enhancing Software Packages
  • Browser
  • Python
  • R
• Machine learning environment management tools
• Free Books
• Government and Regulatory Documents
• Other Interpretability and Fairness Resources and Lists
• Review and General Papers
• Classes
• Interpretable ("Whitebox") or Fair Modeling Packages
  • C/C++
  • Python
  • R
• AI Incident Tracker

Comprehensive Software Examples and Tutorials

• COMPAS Analysis Using Aequitas
• Explaining Quantitative Measures of Fairness (with SHAP)
• Getting a Window into your Black Box Model
• From GLM to GBM Part 1
• From GLM to GBM Part 2
• IML
• Interpretable Machine Learning with Python
• Interpreting Machine Learning Models with the iml Package
• Interpretable Machine Learning using Counterfactuals
• Machine Learning Explainability by Kaggle Learn
• Model Interpretability with DALEX
• Model Interpretation series by Dipanjan (DJ) Sarkar:
  • The Importance of Human Interpretable Machine Learning
  • Model Interpretation Strategies
  • Hands-on Machine Learning Model Interpretation
  • Interpreting Deep Learning Models for Computer Vision
• Partial Dependence Plots in R
• Saliency Maps for Deep Learning
• Visualizing ML Models with LIME
• Visualizing and debugging deep convolutional networks
• What does a CNN see?

Explainability- or Fairness-Enhancing Software Packages

Browser

• DiscriLens
• manifold
• TensorBoard Projector
• What-if Tool

Python

• acd
• aequitas
• AI Fairness 360
• AI Explainability 360
• ALEPython
• Aletheia
• allennlp
• algofairness
• Alibi
• anchor
• BlackBoxAuditing
• casme
• Causal Discovery Toolbox
• captum
• causalml
• cdt15
• checklist
• contextual-AI
• ContrastiveExplanation (Foil Trees)
• counterfit
• dalex
• debiaswe
• DeepExplain
• deeplift
• deepvis
• DiCE
• DoWhy
• ecco
• eli5
• explainerdashboard
• fairml
• fairlearn
• fairness-comparison
• fairness_measures_code
• foolbox
• Grad-CAM (GitHub topic)
• gplearn
• hate-functional-tests
• imodels
• iNNvestigate neural nets
• Integrated-Gradients
• interpret
• interpret_with_rules
• imodels
• Keras-vis
• keract
• L2X
• lime
• LiFT
• lit
• lofo-importance
• lrp_toolbox
• MindsDB
• MLextend
• ml-fairness-gym
• ml_privacy_meter
• OptBinning
• parity-fairness
• PDPbox
• pyBreakDown
• PyCEbox
• pyGAM
• pymc3
• pytorch-innvestigate
• rationale
• responsibly
• revise-tool
• robustness
• RISE
• sage
• SALib
• scikit-fairness
• shap
• shapley
• Skater
• tensorfow/cleverhans
• tensorflow/lucid
• tensorflow/fairness-indicators
• tensorflow/model-analysis
• tensorflow/model-card-toolkit
• tensorflow/model-remediation
• tensorflow/privacy
• tensorflow/tcav
• tensorfuzz
• TensorWatch
• TextFooler
• tf-explain
• Themis
• themis-ml
• treeinterpreter
• woe
• xai
• xdeep
• yellowbrick

R

• aif360
• ALEPlot
• DrWhyAI
• DALEX
• DALEXtra
• EloML
• ExplainPrediction
• fastshap
• fairness
• fairmodels
• featureImportance
• flashlight
• forestmodel
• fscaret
• iBreakDown
• ICEbox
• iml
• ingredients
• intepret
• lightgbmExplainer
• lime
• live
• mcr
• modelDown
• modelOriented
• modelStudio
• pdp
• shapFlex
• shapleyR
• shapper
• smbinning
• vip
• xgboostExplainer

Machine learning environment management tools

• dvc
• gigantum
• mlflow
• mlmd
• modeldb
• whylabs

Free Books

• An Introduction to Machine Learning Interpretability
• Explanatory Model Analysis
• Fairness and Machine Learning
• Interpretable Machine Learning
• Responsible Machine Learning (requires email for now)

Government and Regulatory Documents

• 12 CFR Part 1002 - Equal Credit Opportunity Act (Regulation B)
• A Regulatory Framework for AI: Recommendations for PIPEDA Reform
• AI Principles: Recommendations on the Ethical Use of Artificial Intelligence by the Department of Defense
• THE AIM INITIATIVE
• Aiming for truth, fairness, and equity in your company’s use of AI
• Algorithmic Accountability Act of 2019
• ALGORITHM CHARTER FOR AOTEAROA NEW ZEALAND
• Artificial Intelligence (AI) in the Securities Industry
• Article 22 EU GDPR
• Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment - Shaping Europe’s digital future - European Commission
• Audit of Governance and Protection of Department of Defense Artificial Intelligence Data and Technology
• A Primer on Artificial Intelligence in Securities Markets
• Biometric Information Privacy Act
• Booker Wyden Health Care Letters
• California Consumer Privacy Act (CCPA)
• California Privacy Rights Act (CPRA)
• Consultation on the OPC’s Proposals for ensuring appropriate regulation of artificial intelligence
• Civil liability regime for artificial intelligence
• Data Ethics Framework
• DEVELOPING FINANCIAL SECTOR RESILIENCE IN A DIGITAL WORLD: SELECTED THEMES IN TECHNOLOGY AND RELATED RISKS
• Directive on Automated Decision Making
• Executive Order on Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government
• EEOC Letter (from U.S. senators re: hiring software)
• Facial Recognition and Biometric Technology Moratorium Act of 2020
• Four Principles of Explainable Artificial Intelligence
• General principles for the use of Artificial Intelligence in the financial sector
• Gouvernance des algorithmes d’intelligence artificielle dans le secteur financier (French)
• Innovation spotlight: Providing adverse action notices when using AI/ML models
• Office of Management and Budget Guidance for Regulation of Artificial Intelligence Applications (Finalized Nov. 2020)
• On Artificial Intelligence - A European approach to excellence and trust
• Opinion of the German Data Ethics Commission
• Principles of Artificial Intelligence Ethics for the Intelligence Community
• Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)
• Psychological Foundations of Explainability and Interpretability in Artificial Intelligence
• Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures
• Questions from the Commission on Protecting Privacy and Preventing Discrimination
• RE: Use of External Consumer Data and Information Sources in Underwriting for Life Insurance
• Singapore Personal Data Protection Commission (PDPC) Model Artificial Intelligence Governance Framework
• SUPERVISORY GUIDANCE ON MODEL RISK MANAGEMENT
• U.K. Information Commissioner's Office (ICO) AI Audting Framework (overview series)
• Artificial Intelligence/Machine Learning (AI/ML)-Based: Software as a Medical Device (SaMD) Action Plan (Updated Jan. 2021)
• U.S. House of Representatives Resolution on AI Strategy
• Using Artificial Intelligence and Algorithms

Other Interpretability and Fairness Resources and Lists

• 8 Principles of Responsible ML
• ACM FAT* 2019 Youtube Playlist
• Adversarial ML Threat Matrix
• AI Tools and Platforms
• AI Ethics Guidelines Global Inventory
• AI Incident Database
• AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
• Algorithms and prejudice
• Awesome interpretable machine learning ;)
• Awesome machine learning operations
• Awful AI
• algoaware
• BIML Interactive Machine Learning Risk Framework
• Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models
• criticalML
• Data Feminism
• Dealing with Bias and Fairness in AI/ML/Data Science Systems
• Debugging Machine Learning Models (ICLR workshop proceedings)
• Decision Points in AI Governance
• De-identification Tools
• Deep Insights into Explainability and Interpretability of Machine Learning Algorithms and Applications to Risk Management
• Distill
• Faces in the Wild Benchmark Data
• Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
• From Principles to Practice: An interdisciplinary framework to operationalise AI ethics
• How will the GDPR impact machine learning?
• Machine Learning Ethics References
• Machine Learning Interpretability Resources
• Machine Learning: Considerations for fairly and transparently expanding access to credit
• MIT AI Ethics Reading Group
• On the Responsibility of Technologists: A Prologue and Primer
• private-ai-resources
• Problems with Shapley-value-based explanations as feature importance measures
• Real-World Model Debugging Strategies
• ResponsibleAI
• Robust ML
• Safe and Reliable Machine Learning
• Sample AI Incident Response Checklist
• Ten Questions on AI Risk
• Testing and Debugging in Machine Learning
• Troubleshooting Deep Neural Networks
• Warning Signs: The Future of Privacy and Security in an Age of Machine Learning
• When Not to Trust Your Explanations
• XAI Resources
• You Created A Machine Learning Application Now Make Sure It's Secure

Review and General Papers

• 50 Years of Test (Un)fairness: Lessons for Machine Learning
• 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
• Closing the AI Accountability Gap
• Explaining by Removing: A Unified Framework for Model Explanation
• 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
• Limitations of Interpretable Machine Learning
• Machine Learning Explainability in Finance
• On the Art and Science of Machine Learning Explanations
• Please Stop Explaining Black Box Models for High-Stakes Decisions
• Software Engineering for Machine Learning: A Case Study
• The Mythos of Model Interpretability
• Towards A Rigorous Science of Interpretable Machine Learning
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
• The Security of Machine Learning
• Techniques for Interpretable Machine Learning
• Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda
• Underspecification Presents Challenges for Credibility in Modern Machine Learning

Classes

• An Introduction to Data Ethics
• Certified Ethical Emerging Technologist
• Fairness in Machine Learning
• Fast.ai Data Ethics course
• Human-Center Machine Learning
• Introduction to Responsible Machine Learning
• Trustworthy Deep Learning

Interpretable ("Whitebox") or Fair Modeling Packages

C/C++

• Born-again Tree Ensembles
• Certifiably Optimal RulE ListS

Python

• Bayesian Case Model
• Bayesian Ors-Of-Ands
• Bayesian Rule List (BRL)
• Explainable Boosting Machine (EBM)/GA2M
• fair-classification
• Falling Rule List (FRL)
• H2O-3
  • Penalized Generalized Linear Models
  • Monotonic GBM
  • Sparse Principal Components (GLRM)
• learning-fair-representations
• Optimal Sparse Decision Trees
• Monotonic XGBoost
• Multilayer Logical Perceptron (MLLP)
• pyGAM
• pySS3
• Risk-SLIM
• Scikit-learn
  • Decision Trees
  • Generalized Linear Models
  • Sparse Principal Components
• sklearn-expertsys
• skope-rules
• Super-sparse Linear Integer models (SLIMs)
• tensorflow/lattice
• This Looks Like That

R

• arules
• Causal SVM
• elasticnet
• Explainable Boosting Machine (EBM)/GA2M
• gam
• glm2
• glmnet
• H2O-3
  • Penalized Generalized Linear Models
  • Monotonic GBM
  • Sparse Principal Components (GLRM)
• Monotonic XGBoost
• quantreg
• rpart
• RuleFit
• Scalable Bayesian Rule Lists (SBRL)

AI Incident Tracker

• Mar 1988 - A blot on the profession
• Jan 2010 - Are Face-Detection Cameras Racist?
• Jul 2015 - Google says sorry for racist auto-tag in photo app
• Mar 2016 - Here Are the Microsoft Twitter Bot’s Craziest Racist Rants
• Jun 2016 - Google faulted for racial bias in image search results for black teenagers
• Oct 2016 - 'Rogue' Algorithm Blamed for Historic Crash of the British Pound
• Oct 2016 - Crime-prediction tool PredPol amplifies racially biased policing, study shows
• May 2017 - Houston Schools Must Face Teacher Evaluation Lawsuit
• Jun 2017 - When a Computer Program Keeps You in Jail
• Jun 2017 - Antitrust: Commission fines Google €2.42 billion for abusing dominance as search engine by giving illegal advantage to own comparison shopping service
• Jul 2017 - ‘Balls have zero to me to me’: What happened when Facebook’s AI chatbots Bob & Alice created their own language
• Jul 2017 - YouTube: Boston Dynamics' Atlas Falls Over After Demo at the Congress of Future Scientists and Technologists
• Jul 2017 - Royal Free - Google DeepMind trial failed to comply with data protection law
• Nov 2017 - Hackers Say They've Broken Face ID a Week After iPhone X Release
• Nov 2017 - India’s Friendly Robot Mitra Not Only Greets VIPs On The Stage, But Also Parties Like A Rockstar (Mitra trips over Ivanka Trump/PM Modi introduction)
• Jan 2018 - YouTube: CES 2018: Robot refuses to co-operate with LG chief - BBC News
• Feb 2018 - Study finds gender and skin-type bias in commercial artificial-intelligence systems
• Mar 2018 - Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam
• Mar 2018 - AI-Assisted Fake Porn Is Here and We're All F***ed
• Jun 2018 - Facebook sent a doctor on a secret mission to ask hospitals to share patient data
• Jul 2018 - Amazon’s Face Recognition Falsely Matched 28 Members of Congress With Mugshots
• Jul 2018 - IBM’s Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show
• Oct 2018 - Amazon scraps 'sexist AI' recruiting tool that showed bias against women
• Nov 2018 - Facial recognition system in China mistakes bus ad for jaywalker
• Dec 2018 - AI start-up that scanned babysitters halts launch following Post Report
• Jan 2019 - Cambridge Analytica’s parent pleads guilty to breaking UK data law
• Apr 2019 - Facebook Executive Testifies on AI Failure to Detect the Christchurch Mosque Shooting Video
• May 2019 - Investor Sues After an AI’s Automated Trades Cost Him $20 Million
• May 2019 - Millions of people uploaded photos to the Ever app. Then the company used them to develop facial recognition tools.
• Jun 2019 - Google and the University of Chicago Are Sued Over Data Sharing
• Aug 2019 - LGBTQ+ creators file lawsuit against YouTube for discrimination
• Sep 2019 - The viral selfie app ImageNet Roulette seemed fun – until it called me a racist slur
• Sep 2019 - Scammer Successfully Deepfaked CEO's Voice To Fool Underling Into Transferring $243,000
• Oct 2019 - Oh dear... AI models used to flag hate speech online are, er, racist against black people
• Oct 2019 - Dissecting racial bias in an algorithm used to manage the health of populations
• Nov 2019 - NY regulator investigating Apple Card for possible gender bias
• Nov 2019 - Chinese-style facial recognition technology is trialled in Australian schools to register pupils - sparking major privacy concerns
• Dec 2019 - Tenants sounded the alarm on facial recognition in their buildings. Lawmakers are listening
• Dec 2019 - Researchers bypass airport and payment facial recognition systems using masks
• Jan 2020 - Atlantic Plaza Towers tenants won a halt to facial recognition in their building: Now they’re calling on a moratorium on all residential use
• Jan 2020 - Trivago misled consumers about hotel room rates
• Feb 2020 - An Indian politician is using deepfake technology to win new voters
• Feb 2020 - Suckers List: How Allstate’s Secret Auto Insurance Algorithm Squeezes Big Spenders
• Feb 2020 - Tesla Autopilot gets tricked into accelerating from 35 to 85 mph with modified speed limit sign
• Mar 2020 - Netherlands: Court Prohibits Government’s Use of AI Software to Detect Welfare Fraud
• Mar 2020 - The End of Starsky Robotics
• Apr 2020 - Google apologizes after its Vision AI produced racist results
• Apr 2020 - Google’s medical AI was super accurate in a lab. Real life was a different story.

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