Awesome Interpretable Machine Learning

Opinionated list of resources facilitating model interpretability (introspection, simplification, visualization, explanation).

Interpretable Models

• Interpretable models
  • Simple decision trees
  • Rules
  • (Regularized) linear regression
  • k-NN
• (2008) Predictive learning via rule ensembles by Jerome H. Friedman, Bogdan E. Popescu
  • https://dx.doi.org/10.1214/07-AOAS148
• (2014) Comprehensible classification models by Alex A. Freitas
  • https://dx.doi.org/10.1145/2594473.2594475
  • http://www.kdd.org/exploration_files/V15-01-01-Freitas.pdf
  • Interesting discussion of interpretability for a few classification models (decision trees, classification rules, decision tables, nearest neighbors and Bayesian network classifier)
• (2015) Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model by Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
  • https://arxiv.org/pdf/1511.01644
  • https://dx.doi.org/10.1214/15-AOAS848
• (2017) Learning Explanatory Rules from Noisy Data by Richard Evans, Edward Grefenstette
  • https://arxiv.org/pdf/1711.04574
• (2019) Transparent Classification with Multilayer Logical Perceptrons and Random Binarization by Zhuo Wang, Wei Zhang, Ning Liu, Jianyong Wang
  • https://arxiv.org/pdf/1912.04695
  • Code: https://github.com/12wang3/mllp

Feature Importance

• Models offering feature importance measures
  • Random forest
  • Boosted trees
  • Extremely randomized trees
    • (2006) Extremely randomized trees by Pierre Geurts, Damien Ernst, Louis Wehenkel
      • https://dx.doi.org/10.1007/s10994-006-6226-1
  • Random ferns
    • (2015) rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning by Miron B. Kursa
      • https://dx.doi.org/10.18637/jss.v061.i10
      • https://cran.r-project.org/web/packages/rFerns
      • https://notabug.org/mbq/rFerns
  • Linear regression (with a grain of salt)
• (2007) Bias in random forest variable importance measures: Illustrations, sources and a solution by Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, Torsten Hothorn
  • https://dx.doi.org/10.1186/1471-2105-8-25
• (2008) Conditional Variable Importance for Random Forests by Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, Achim Zeileis
  • https://dx.doi.org/10.1186/1471-2105-9-307
• (2018) Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the “Rashomon” Perspective by Aaron Fisher, Cynthia Rudin, Francesca Dominici
  • https://arxiv.org/pdf/1801.01489
  • https://github.com/aaronjfisher/mcr
  • Universal (model agnostic) variable importance measure
• (2019) Please Stop Permuting Features: An Explanation and Alternatives by Giles Hooker, Lucas Mentch
  • https://arxiv.org/pdf/1905.03151
  • Paper advocating against feature permutation for importance
• (2018) Visualizing the Feature Importance for Black Box Models by Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
  • https://arxiv.org/pdf/1804.06620
  • https://github.com/giuseppec/featureImportance
  • Global and local (model agnostic) variable importance measure (based on Model Reliance)
• Very good blog post describing deficiencies of random forest feature importance and the permutation importance
  • http://explained.ai/rf-importance/index.html
• Permutation importance - simple model agnostic approach is described in Eli5 documentation
  • https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html

Feature Selection

• Classification of feature selection methods
  • Filters
  • Wrappers
  • Embedded methods
• (2003) An Introduction to Variable and Feature Selection by Isabelle Guyon, André Elisseeff
  • http://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
  • Be sure to read this very illustrative introduction to feature selection
• Filter Methods
  • (2006) On the Use of Variable Complementarity for Feature Selection in Cancer Classification by Patrick Meyer, Gianluca Bontempi
    • https://dx.doi.org/10.1007/11732242_9
    • https://pdfs.semanticscholar.org/d72f/f5063520ce4542d6d9b9e6a4f12aafab6091.pdf
    • Introduces information theoretic methods - double input symmetrical relevance (DISR)
  • (2012) Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection by Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luján
    • http://www.jmlr.org/papers/volume13/brown12a/brown12a.pdf
    • Code: https://github.com/Craigacp/FEAST
    • Discusses various approaches based on mutual information (MIM, mRMR, MIFS, CMIM, JMI, DISR, ICAP, CIFE, CMI)
  • (2012) Feature selection via joint likelihood by Adam Pocock
    • http://www.cs.man.ac.uk/~gbrown/publications/pocockPhDthesis.pdf
  • (2017) Relief-Based Feature Selection: Introduction and Review by Ryan J. Urbanowicz, Melissa Meeker, William LaCava, Randal S. Olson, Jason H. Moore
    • https://arxiv.org/pdf/1711.08421
  • (2017) Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining by Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore
    • https://arxiv.org/pdf/1711.08477
• Wrapper methods
  • (2015) Feature Selection with theBorutaPackage by Miron B. Kursa, Witold R. Rudnicki
    • https://dx.doi.org/10.18637/jss.v036.i11
    • https://cran.r-project.org/web/packages/Boruta/
    • Code (official, R): https://notabug.org/mbq/Boruta/
    • Code (Python): https://github.com/scikit-learn-contrib/boruta_py
  • Boruta for those in a hurry
    • https://cran.r-project.org/web/packages/Boruta/vignettes/inahurry.pdf
• General
  • (1994) Irrelevant Features and the Subset Selection Problem by George John, Ron Kohavi, Karl Pfleger
    • https://pdfs.semanticscholar.org/a83b/ddb34618cc68f1014ca12eef7f537825d104.pdf
    • Classic paper discussing weakly relevant features, irrelevant features, strongly relevant features
  • (2003) Special issue of JMLR of feature selection - oldish (2003)
    • http://www.jmlr.org/papers/special/feature03.html
  • (2004) Result Analysis of the NIPS 2003 Feature Selection Challenge by Isabelle Guyon, Steve Gunn, Asa Ben-Hur, Gideon Dror
    • Paper: https://papers.nips.cc/paper/2728-result-analysis-of-the-nips-2003-feature-selection-challenge.pdf
    • Website http://clopinet.com/isabelle/Projects/NIPS2003/
  • (2007) Consistent Feature Selection for Pattern Recognition in Polynomial Time by Roland Nilsson, José Peña, Johan Björkegren, Jesper Tegnér
    • http://www.jmlr.org/papers/volume8/nilsson07a/nilsson07a.pdf
    • Discusses minimal optimal vs all-relevant approaches to feature selection
• Feature Engineering and Selection by Kuhn & Johnson
  • Sligtly off-topic, but very interesting book
  • http://www.feat.engineering/index.html
  • https://bookdown.org/max/FES/
  • https://github.com/topepo/FES
• Feature Engineering presentation by H. J. van Veen
  • Slightly off-topicm but very interesting deck of slides
  • Slides: https://www.slideshare.net/HJvanVeen/feature-engineering-72376750

Model Explanations

Philosophy

• Magnets by R. P. Feynman https://www.youtube.com/watch?v=wMFPe-DwULM
• (2002) Looking Inside the Black Box, presentation of Leo Breiman
  • https://www.stat.berkeley.edu/users/breiman/wald2002-2.pdf
• (2011) To Explain or to Predict? by Galit Shmueli
  • https://arxiv.org/pdf/1101.0891
  • https://dx.doi.org/10.1214/10-STS330
• (2016) The Mythos of Model Interpretability by Zachary C. Lipton
  • https://arxiv.org/pdf/1606.03490
  • https://www.youtube.com/watch?v=mvzBQci04qA
• (2017) Towards A Rigorous Science of Interpretable Machine Learning by Finale Doshi-Velez, Been Kim
  • https://arxiv.org/pdf/1702.08608
• (2017) The Promise and Peril of Human Evaluation for Model Interpretability by Bernease Herman
  • https://arxiv.org/pdf/1711.07414
• (2018) The Book of Why: The New Science of Cause and Effect by Judea Pearl
• (2018) Please Stop Doing the “Explainable” ML by Cynthia Rudin
  • Video (starts 17:30, lasts 10 min): https://zoom.us/recording/play/0y-iI9HamgyDzzP2k_jiTu6jB7JgVVXnjWZKDMbnyRTn3FsxTDZy6Wkrj3_ekx4J
  • Linked at: https://users.cs.duke.edu/~cynthia/mediatalks.html
• (2018) Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning by Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, Lalana Kagal
  • https://arxiv.org/pdf/1806.00069
• (2019) Interpretable machine learning: definitions, methods, and applications by W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, Bin Yu
  • https://arxiv.org/pdf/1901.04592
• (2019) On Explainable Machine Learning Misconceptions A More Human-Centered Machine Learning by Patrick Hall
  • https://github.com/jphall663/xai_misconceptions/blob/master/xai_misconceptions.pdf
  • https://github.com/jphall663/xai_misconceptions
• (2019) An Introduction to Machine Learning Interpretability. An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI by Patrick Hall and Navdeep Gill
  • https://www.h2o.ai/wp-content/uploads/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf

Model Agnostic Explanations

• (2009) How to Explain Individual Classification Decisions by David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Mueller
  • https://arxiv.org/pdf/0912.1128
• (2013) Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation by Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin
  • https://arxiv.org/pdf/1309.6392
• (2016) “Why Should I Trust You?”: Explaining the Predictions of Any Classifier by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
  • https://arxiv.org/pdf/1602.04938
  • Code: https://github.com/marcotcr/lime
  • https://github.com/marcotcr/lime-experiments
  • https://www.youtube.com/watch?v=bCgEP2zuYxI
  • Introduces the LIME method (Local Interpretable Model-agnostic Explanations)
• (2016) A Model Explanation System: Latest Updates and Extensions by Ryan Turner
  • https://arxiv.org/pdf/1606.09517
  • http://www.blackboxworkshop.org/pdf/Turner2015_MES.pdf
• (2017) Understanding Black-box Predictions via Influence Functions by Pang Wei Koh, Percy Liang
  • https://arxiv.org/pdf/1703.04730
• (2017) A Unified Approach to Interpreting Model Predictions by Scott Lundberg, Su-In Lee
  • https://arxiv.org/pdf/1705.07874
  • Code: https://github.com/slundberg/shap
  • Introduces the SHAP method (SHapley Additive exPlanations), generalizing LIME
• (2018) Anchors: High-Precision Model-Agnostic Explanations by Marco Ribeiro, Sameer Singh, Carlos Guestrin
  • https://homes.cs.washington.edu/~marcotcr/aaai18.pdf
  • Code: https://github.com/marcotcr/anchor-experiments
• (2018) Learning to Explain: An Information-Theoretic Perspective on Model Interpretation by Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan
  • https://arxiv.org/pdf/1802.07814
• (2018) Explanations of model predictions with live and breakDown packages by Mateusz Staniak, Przemyslaw Biecek
  • https://arxiv.org/pdf/1804.01955
  • Docs: https://mi2datalab.github.io/live/
  • Code: https://github.com/MI2DataLab/live
  • Docs: https://pbiecek.github.io/breakDown
  • Code: https://github.com/pbiecek/breakDown
• (2018) A review book - Interpretable Machine Learning. A Guide for Making Black Box Models Explainable by Christoph Molnar
  • https://christophm.github.io/interpretable-ml-book/
• (2018) Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead by Cynthia Rudin
  • https://arxiv.org/pdf/1811.10154
• (2019) Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition by Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
  • https://arxiv.org/pdf/1904.03867

Model Specific Explanations - Neural Networks

• (2013) Visualizing and Understanding Convolutional Networks by Matthew D Zeiler, Rob Fergus
  • https://arxiv.org/pdf/1311.2901
• (2013) Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps by Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
  • https://arxiv.org/pdf/1312.6034
• (2015) Understanding Neural Networks Through Deep Visualization by Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, Hod Lipson
  • https://arxiv.org/pdf/1506.06579
  • https://github.com/yosinski/deep-visualization-toolbox
• (2016) Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization by Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra
  • https://arxiv.org/pdf/1610.02391
• (2016) Generating Visual Explanations by Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, Trevor Darrell
  • https://arxiv.org/pdf/1603.08507
• (2016) Rationalizing Neural Predictions by Tao Lei, Regina Barzilay, Tommi Jaakkola
  • https://arxiv.org/pdf/1606.04155
  • https://people.csail.mit.edu/taolei/papers/emnlp16_rationale_slides.pdf
  • Code: https://github.com/taolei87/rcnn/tree/master/code/rationale
• (2016) Gradients of Counterfactuals by Mukund Sundararajan, Ankur Taly, Qiqi Yan
  • https://arxiv.org/pdf/1611.02639
• Pixel entropy can be used to detect relevant picture regions (for CovNets)
  • See Visualization section and Fig. 5 of the paper
    • (2017) High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks by Krzysztof J. Geras, Stacey Wolfson, Yiqiu Shen, Nan Wu, S. Gene Kim, Eric Kim, Laura Heacock, Ujas Parikh, Linda Moy, Kyunghyun Cho
      • https://arxiv.org/pdf/1703.07047
• (2017) SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability by Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein
  • https://arxiv.org/pdf/1706.05806
  • https://research.googleblog.com/2017/11/interpreting-deep-neural-networks-with.html
• (2017) Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks by Jose Oramas, Kaili Wang, Tinne Tuytelaars
  • https://arxiv.org/pdf/1712.06302
• (2017) Axiomatic Attribution for Deep Networks by Mukund Sundararajan, Ankur Taly, Qiqi Yan
  • https://arxiv.org/pdf/1703.01365
  • Code: https://github.com/ankurtaly/Integrated-Gradients
  • Proposes Integrated Gradients Method
  • See also: Gradients of Counterfactuals https://arxiv.org/pdf/1611.02639.pdf
• (2017) Learning Important Features Through Propagating Activation Differences by Avanti Shrikumar, Peyton Greenside, Anshul Kundaje
  • https://arxiv.org/pdf/1704.02685
  • Proposes Deep Lift method
  • Code: https://github.com/kundajelab/deeplift
  • Videos: https://www.youtube.com/playlist?list=PLJLjQOkqSRTP3cLB2cOOi_bQFw6KPGKML
• (2017) The (Un)reliability of saliency methods by Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim
  • https://arxiv.org/pdf/1711.0867
  • Review of failures for methods extracting most important pixels for prediction
• (2018) Classifier-agnostic saliency map extraction by Konrad Zolna, Krzysztof J. Geras, Kyunghyun Cho
  • https://arxiv.org/pdf/1805.08249
  • Code: https://github.com/kondiz/casme
• (2018) A Benchmark for Interpretability Methods in Deep Neural Networks by Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
  • https://arxiv.org/pdf/1806.10758
• (2018) The Building Blocks of Interpretability by Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, Alexander Mordvintsev
  • https://dx.doi.org/10.23915/distill.00010
  • Has some embeded links to notebooks
  • Uses Lucid library https://github.com/tensorflow/lucid
• (2018) Hierarchical interpretations for neural network predictions by Chandan Singh, W. James Murdoch, Bin Yu
  • https://arxiv.org/pdf/1806.05337
  • Code: https://github.com/csinva/hierarchical_dnn_interpretations
• (2018) iNNvestigate neural networks! by Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans
  • https://arxiv.org/pdf/1808.04260
  • Code: https://github.com/albermax/innvestigate
• (2018) YASENN: Explaining Neural Networks via Partitioning Activation Sequences by Yaroslav Zharov, Denis Korzhenkov, Pavel Shvechikov, Alexander Tuzhilin
  • https://arxiv.org/pdf/1811.02783
• (2019) Attention is not Explanation by Sarthak Jain, Byron C. Wallace
  • https://arxiv.org/pdf/1902.10186
• (2019) Attention Interpretability Across NLP Tasks by Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, Manaal Faruqui
  • https://arxiv.org/pdf/1909.11218
• (2019) GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction by Thai Le, Suhang Wang, Dongwon Lee
  • https://arxiv.org/pdf/1911.02042
  • Code: https://github.com/lethaiq/GRACE_KDD20

Extracting Interpretable Models From Complex Ones

• (2017) Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples by Gail Weiss, Yoav Goldberg, Eran Yahav
  • https://arxiv.org/pdf/1711.09576
• (2017) Distilling a Neural Network Into a Soft Decision Tree by Nicholas Frosst, Geoffrey Hinton
  • https://arxiv.org/pdf/1711.09784
• (2017) Detecting Bias in Black-Box Models Using Transparent Model Distillation by Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou
  • http://www.aies-conference.com/2018/contents/papers/main/AIES_2018_paper_96.pdf

Model Visualization

• Visualizing Statistical Models: Removing the blindfold
  • http://had.co.nz/stat645/model-vis.pdf
• Partial dependence plots
  • http://scikit-learn.org/stable/auto_examples/ensemble/plot_partial_dependence.html
  • pdp: An R Package for Constructing Partial Dependence Plots https://journal.r-project.org/archive/2017/RJ-2017-016/RJ-2017-016.pdf https://cran.r-project.org/web/packages/pdp/index.html
• ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages
  • https://journal.r-project.org/archive/2016-2/tang-horikoshi-li.pdf
  • CRAN https://cran.r-project.org/web/packages/ggfortify/index.html
• RandomForestExplainer
  • Master thesis https://rawgit.com/geneticsMiNIng/BlackBoxOpener/master/randomForestExplainer_Master_thesis.pdf
  • R code
    • CRAN https://cran.r-project.org/web/packages/randomForestExplainer/index.html
    • Code: https://github.com/MI2DataLab/randomForestExplainer
• ggRandomForest
  • Paper (vignette) https://github.com/ehrlinger/ggRandomForests/raw/master/vignettes/randomForestSRC-Survival.pdf
  • R code
    • CRAN https://cran.r-project.org/web/packages/ggRandomForests/index.html
    • Code: https://github.com/ehrlinger/ggRandomForests

Selected Review Talks and Tutorials

• Tutorial on Interpretable machine learning at ICML 2017
  • Slides: http://people.csail.mit.edu/beenkim/papers/BeenK_FinaleDV_ICML2017_tutorial.pdf
• P. Biecek, Show Me Your Model - Tools for Visualisation of Statistical Models
  • Video: https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/Show-Me-Your-Model-tools-for-visualisation-of-statistical-models
• S. Ritchie, Just-So Stories of AI
  • Video: https://www.youtube.com/watch?v=DiWkKqZChF0
  • Slides: https://speakerdeck.com/sritchie/just-so-stories-for-ai-explaining-black-box-predictions
• C. Jarmul, Towards Interpretable Accountable Models
  • Video: https://www.youtube.com/watch?v=B3PtcF-6Dtc
  • Slides: https://docs.google.com/presentation/d/e/2PACX-1vR05kpagAbL5qo1QThxwu44TI5SQAws_UFVg3nUAmKp39uNG0xdBjcMA-VyEeqZRGGQtt0CS5h2DMTS/embed?start=false&loop=false&delayms=3000
• I. Oszvald, Machine Learning Libraries You’d Wish You’d Known About
  • A large part of the talk covers model explanation and visualization
  • Video: https://www.youtube.com/watch?v=nDF7_8FOhpI
  • Associated notebook on explaining regression predictions: https://github.com/ianozsvald/data_science_delivered/blob/master/ml_explain_regression_prediction.ipynb
• G. Varoquaux, Understanding and diagnosing your machine-learning models (covers PDP and Lime among others)
  • Video: https://www.youtube.com/watch?v=kbj3llSbaVA
  • Slides: http://gael-varoquaux.info/interpreting_ml_tuto/

Venues

• Interpretable ML Symposium (NIPS 2017) (contains links to papers, slides and videos)
  • http://interpretable.ml/
  • Debate, Interpretability is necessary in machine learning
    • https://www.youtube.com/watch?v=2hW05ZfsUUo
• Workshop on Human Interpretability in Machine Learning (WHI), organised in conjunction with ICML
  • 2018 (contains links to papers and slides)
    • https://sites.google.com/view/whi2018
    • Proceedings https://arxiv.org/html/1807.01308
  • 2017 (contains links to papers and slides)
    • https://sites.google.com/view/whi2017/home
    • Proceedings https://arxiv.org/html/1708.02666
  • 2016 (contains links to papers)
    • https://sites.google.com/site/2016whi/
    • Proceedings https://arxiv.org/html/1607.02531 or here
• Analyzing and interpreting neural networks for NLP (BlackboxNLP), organised in conjunction with EMNLP
  • 2019 (links below may get prefixed by 2019 later on)
    • https://blackboxnlp.github.io/
    • https://blackboxnlp.github.io/program.html
    • Papers should be available on arXiv
  • 2018
    • https://blackboxnlp.github.io/2018
    • https://blackboxnlp.github.io/program.html
    • List of papers
• FAT/ML Fairness, Accountability, and Transparency in Machine Learning https://www.fatml.org/
  • 2018
    • https://www.fatml.org/schedule/2018
  • 2017
    • https://www.fatml.org/schedule/2017
  • 2016
    • https://www.fatml.org/schedule/2016
  • 2016
    • https://www.fatml.org/schedule/2016
  • 2015
    • https://www.fatml.org/schedule/2015
  • 2014
    • https://www.fatml.org/schedule/2014
  • AAAI/ACM Annual Conferenceon AI, Ethics, and Society
    • 2019 (links below may get prefixed by 2019 later on)
      • http://www.aies-conference.com/accepted-papers/
    • 2018
      • http://www.aies-conference.com/2018/accepted-papers/
      • http://www.aies-conference.com/2018/accepted-student-papers/

Software

Software related to papers is mentioned along with each publication. Here only standalone software is included.

• DALEX - R package, Descriptive mAchine Learning EXplanations
  • CRAN https://cran.r-project.org/web/packages/DALEX/DALEX.pdf
  • Code: https://github.com/pbiecek/DALEX
• ELI5 - Python package dedicated to debugging machine learning classifiers and explaining their predictions
  • Code: https://github.com/TeamHG-Memex/eli5
  • https://eli5.readthedocs.io/en/latest/
• forestmodel - R package visualizing coefficients of different models with the so called forest plot
  • CRAN https://cran.r-project.org/web/packages/forestmodel/index.html
  • Code: https://github.com/NikNakk/forestmodel
• fscaret - R package with automated Feature Selection from ‘caret’
  • CRAN https://cran.r-project.org/web/packages/fscaret/
  • Tutorial: https://cran.r-project.org/web/packages/fscaret/vignettes/fscaret.pdf
• iml - R package for Interpretable Machine Learning
  • CRAN https://cran.r-project.org/web/packages/iml/
  • Code: https://github.com/christophM/iml
  • Publication: http://joss.theoj.org/papers/10.21105/joss.00786
• interpret - Python package package for training interpretable models and explaining blackbox systems by Microsoft
  • Code: https://github.com/microsoft/interpret
• lime - R package implementing LIME
  • https://github.com/thomasp85/lime
• lofo-importance - Python package feature importance by Leave One Feature Out Importance method
  • Code: https://github.com/aerdem4/lofo-importance
• Lucid - a collection of infrastructure and tools for research in neural network interpretability
  • Code: https://github.com/tensorflow/lucid
• praznik - R package with a collection of feature selection filters performing greedy optimisation of mutual information-based usefulness criteria, see JMLR 13, 27−66 (2012)
  • CRAN https://cran.r-project.org/web/packages/praznik/index.html
  • Code: https://notabug.org/mbq/praznik
• yellowbrick - Python package offering visual analysis and diagnostic tools to facilitate machine learning model selection
  • Code: https://github.com/DistrictDataLabs/yellowbrick
  • http://www.scikit-yb.org/en/latest/

Other Resources

• Awesome list of resources by Patrick Hall
  • https://github.com/jphall663/awesome-machine-learning-interpretability
• Awesome XAI resources by Przemysław Biecek
  • https://github.com/pbiecek/xai_resources