{"id":13408531,"url":"https://github.com/lopusz/awesome-interpretable-machine-learning","last_synced_at":"2026-01-18T10:31:33.972Z","repository":{"id":63787424,"uuid":"115538244","full_name":"lopusz/awesome-interpretable-machine-learning","owner":"lopusz","description":null,"archived":false,"fork":false,"pushed_at":"2023-03-19T09:28:48.000Z","size":1542,"stargazers_count":918,"open_issues_count":2,"forks_count":141,"subscribers_count":50,"default_branch":"master","last_synced_at":"2025-11-08T10:01:53.505Z","etag":null,"topics":["data-science","explainable-ai","interpretable-ai","interpretable-machine-learning","interpretable-ml","machine-learning","xai"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lopusz.png","metadata":{"files":{"readme":"README.org","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2017-12-27T16:21:24.000Z","updated_at":"2025-10-27T21:40:16.000Z","dependencies_parsed_at":"2024-01-06T20:53:03.196Z","dependency_job_id":"ba8d68de-98ba-471b-9199-0d77853d3f77","html_url":"https://github.com/lopusz/awesome-interpretable-machine-learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/lopusz/awesome-interpretable-machine-learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lopusz%2Fawesome-interpretable-machine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lopusz%2Fawesome-interpretable-machine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lopusz%2Fawesome-interpretable-machine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lopusz%2Fawesome-interpretable-machine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lopusz","download_url":"https://codeload.github.com/lopusz/awesome-interpretable-machine-learning/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lopusz%2Fawesome-interpretable-machine-learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28534447,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-18T10:13:46.436Z","status":"ssl_error","status_checked_at":"2026-01-18T10:13:11.045Z","response_time":98,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","explainable-ai","interpretable-ai","interpretable-machine-learning","interpretable-ml","machine-learning","xai"],"created_at":"2024-07-30T20:00:53.445Z","updated_at":"2026-01-18T10:31:33.930Z","avatar_url":"https://github.com/lopusz.png","language":"Python","funding_links":[],"categories":["Interpretability Lists","Acceleration of Feature Interaction Detection","Related Repositories","AI Incidents, Critiques, and Research Resources","Uncategorized","Core Machine Learning Research"],"sub_categories":["Reinforcement Method","Evaluation methods","List of Lists","Uncategorized","Robustness, Interpretability, and Learning Paradigms"],"readme":"* Awesome Interpretable Machine Learning [[https://awesome.re][https://awesome.re/badge.svg]]\n\nOpinionated list of resources facilitating model interpretability\n(introspection, simplification, visualization, explanation).\n\n** Interpretable Models\n   + Interpretable models\n     + Simple decision trees\n     + Rules\n     + (Regularized) linear regression\n     + k-NN\n\n   + (2008) Predictive learning via rule ensembles by Jerome H. Friedman, Bogdan E. Popescu\n     + https://dx.doi.org/10.1214/07-AOAS148\n\n   + (2014) Comprehensible classification models by Alex A. Freitas\n     + https://dx.doi.org/10.1145/2594473.2594475\n     + http://www.kdd.org/exploration_files/V15-01-01-Freitas.pdf\n     + Interesting discussion of interpretability for a few  classification  models\n       (decision trees, classification rules, decision tables, nearest neighbors  and  Bayesian  network  classifier)\n\n   + (2015) Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model by Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan\n     + https://arxiv.org/pdf/1511.01644\n     + https://dx.doi.org/10.1214/15-AOAS848\n\n   + (2017) Learning Explanatory Rules from Noisy Data by Richard Evans, Edward Grefenstette\n     + https://arxiv.org/pdf/1711.04574\n\n   + (2019) Transparent Classification with Multilayer Logical Perceptrons and Random Binarization by Zhuo Wang, Wei Zhang, Ning Liu, Jianyong Wang\n     + https://arxiv.org/pdf/1912.04695\n     + Code: https://github.com/12wang3/mllp\n\n** Feature Importance\n   + Models offering feature importance measures\n     + Random forest\n     + Boosted trees\n     + Extremely randomized trees\n       + (2006) Extremely randomized trees by Pierre Geurts, Damien Ernst, Louis Wehenkel\n         + https://dx.doi.org/10.1007/s10994-006-6226-1\n     + Random ferns\n       + (2015) rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning by Miron B. Kursa\n         + https://dx.doi.org/10.18637/jss.v061.i10\n         + https://cran.r-project.org/web/packages/rFerns\n         + https://notabug.org/mbq/rFerns\n     + Linear regression (with a grain of salt)\n\n   + (2007) Bias in random forest variable importance measures: Illustrations, sources and a solution by Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, Torsten Hothorn\n     + https://dx.doi.org/10.1186/1471-2105-8-25\n\n   + (2008) Conditional Variable Importance for Random Forests by Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, Achim Zeileis\n     + https://dx.doi.org/10.1186/1471-2105-9-307\n\n   + (2018) Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the \"Rashomon\" Perspective by Aaron Fisher, Cynthia Rudin, Francesca Dominici\n     + https://arxiv.org/pdf/1801.01489\n     + https://github.com/aaronjfisher/mcr\n     + Universal (model agnostic) variable importance measure\n\n   + (2019) Please Stop Permuting Features: An Explanation and Alternatives by Giles Hooker, Lucas Mentch\n     + https://arxiv.org/pdf/1905.03151\n     + Paper advocating against feature permutation for importance\n\n   + (2018) Visualizing the Feature Importance for Black Box Models by Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl\n     + https://arxiv.org/pdf/1804.06620\n     + https://github.com/giuseppec/featureImportance\n     + Global and local (model agnostic) variable importance measure (based on Model Reliance)\n\n   + Very good blog post describing deficiencies of random forest feature importance and the permutation importance\n     + http://explained.ai/rf-importance/index.html\n\n   + Permutation importance - simple model agnostic approach is described in Eli5 documentation\n     + https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html\n\n** Feature Selection\n   + Classification of feature selection methods\n     + Filters\n     + Wrappers\n     + Embedded methods\n\n   + (2003) An Introduction to Variable and Feature Selection by Isabelle Guyon, André Elisseeff\n     + http://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf\n     + Be sure to read this very illustrative introduction to feature selection\n\n   + Filter Methods\n\n     + (2006) On the Use of Variable Complementarity for Feature Selection in Cancer Classification by Patrick Meyer, Gianluca Bontempi\n       + https://dx.doi.org/10.1007/11732242_9\n       + https://pdfs.semanticscholar.org/d72f/f5063520ce4542d6d9b9e6a4f12aafab6091.pdf\n       + Introduces information theoretic methods - double input symmetrical relevance (DISR)\n\n     + (2012) Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection by Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luján\n       + http://www.jmlr.org/papers/volume13/brown12a/brown12a.pdf\n       + Code: https://github.com/Craigacp/FEAST\n       + Discusses various approaches based on mutual information (MIM, mRMR, MIFS, CMIM, JMI, DISR, ICAP, CIFE, CMI)\n\n     + (2012) Feature selection via joint likelihood by Adam Pocock\n       + http://www.cs.man.ac.uk/~gbrown/publications/pocockPhDthesis.pdf\n\n     + (2017) Relief-Based Feature Selection: Introduction and Review by Ryan J. Urbanowicz, Melissa Meeker, William LaCava, Randal S. Olson, Jason H. Moore\n       + https://arxiv.org/pdf/1711.08421\n\n     + (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\n       + https://arxiv.org/pdf/1711.08477\n\n   + Wrapper methods\n\n     + (2015) Feature Selection with theBorutaPackage by Miron B. Kursa, Witold R. Rudnicki\n       + https://dx.doi.org/10.18637/jss.v036.i11\n       + https://cran.r-project.org/web/packages/Boruta/\n       + Code (official, R): https://notabug.org/mbq/Boruta/\n       + Code (Python): https://github.com/scikit-learn-contrib/boruta_py\n\n     + Boruta for those in a hurry\n       + https://cran.r-project.org/web/packages/Boruta/vignettes/inahurry.pdf\n\n   + General\n\n     + (1994) Irrelevant Features and the Subset Selection Problem by George John, Ron Kohavi, Karl Pfleger\n       + https://pdfs.semanticscholar.org/a83b/ddb34618cc68f1014ca12eef7f537825d104.pdf\n       + Classic paper discussing weakly relevant features, irrelevant features, strongly relevant features\n\n     + (2003) Special issue of JMLR of feature selection - oldish (2003)\n       + http://www.jmlr.org/papers/special/feature03.html\n\n     + (2004) Result Analysis of the NIPS 2003 Feature Selection Challenge by Isabelle Guyon, Steve Gunn, Asa Ben-Hur, Gideon Dror\n       + Paper: https://papers.nips.cc/paper/2728-result-analysis-of-the-nips-2003-feature-selection-challenge.pdf\n       + Website http://clopinet.com/isabelle/Projects/NIPS2003/\n\n     + (2007) Consistent Feature Selection for Pattern Recognition in Polynomial Time by Roland Nilsson, José Peña, Johan Björkegren, Jesper Tegnér\n       + http://www.jmlr.org/papers/volume8/nilsson07a/nilsson07a.pdf\n       + Discusses minimal optimal vs all-relevant approaches to feature selection\n\n   + Feature Engineering and Selection by Kuhn \u0026 Johnson\n     + Sligtly off-topic, but very interesting book\n     + http://www.feat.engineering/index.html\n     + https://bookdown.org/max/FES/\n     + https://github.com/topepo/FES\n\n   + Feature Engineering presentation by H. J. van Veen\n     + Slightly off-topicm but very interesting deck of slides\n     + Slides: https://www.slideshare.net/HJvanVeen/feature-engineering-72376750\n\n** Model Explanations\n*** Philosophy\n    + Magnets by R. P. Feynman\n      https://www.youtube.com/watch?v=wMFPe-DwULM\n\n    + (2002) Looking Inside the Black Box, presentation of Leo Breiman\n      + https://www.stat.berkeley.edu/users/breiman/wald2002-2.pdf\n\n    + (2011) To Explain or to Predict? by Galit Shmueli\n      + https://arxiv.org/pdf/1101.0891\n      + https://dx.doi.org/10.1214/10-STS330\n\n    + (2016) The Mythos of Model Interpretability by Zachary C. Lipton\n      + https://arxiv.org/pdf/1606.03490\n      + https://www.youtube.com/watch?v=mvzBQci04qA\n\n    + (2017) Towards A Rigorous Science of Interpretable Machine Learning by Finale Doshi-Velez, Been Kim\n      + https://arxiv.org/pdf/1702.08608\n\n    + (2017) The Promise and Peril of Human Evaluation for Model Interpretability by Bernease Herman\n      + https://arxiv.org/pdf/1711.07414\n\n    + (2018) [[http://bayes.cs.ucla.edu/WHY/why-intro.pdf][The Book of Why: The New Science of Cause and Effect]] by Judea Pearl\n\n    + (2018) Please Stop Doing the \"Explainable\" ML by Cynthia Rudin\n      + Video (starts 17:30, lasts 10 min): https://zoom.us/recording/play/0y-iI9HamgyDzzP2k_jiTu6jB7JgVVXnjWZKDMbnyRTn3FsxTDZy6Wkrj3_ekx4J\n      + Linked at: https://users.cs.duke.edu/~cynthia/mediatalks.html\n\n    + (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\n      + https://arxiv.org/pdf/1806.00069\n\n    + (2019) Interpretable machine learning: definitions, methods, and applications by W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, Bin Yu\n      + https://arxiv.org/pdf/1901.04592\n\n    + (2019) On Explainable Machine Learning Misconceptions A More Human-Centered Machine Learning by Patrick Hall\n      + https://github.com/jphall663/xai_misconceptions/blob/master/xai_misconceptions.pdf\n      + https://github.com/jphall663/xai_misconceptions\n\n    + (2019) An Introduction to Machine Learning Interpretability. An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI by Patrick Hall and Navdeep Gill\n      + https://www.h2o.ai/wp-content/uploads/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf\n\n*** Model Agnostic Explanations\n    + (2009) How to Explain Individual Classification Decisions by David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Mueller\n      + https://arxiv.org/pdf/0912.1128\n\n    + (2013) Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation by Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin\n      + https://arxiv.org/pdf/1309.6392\n\n    + (2016) \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin\n      + https://arxiv.org/pdf/1602.04938\n      + Code: https://github.com/marcotcr/lime\n      + https://github.com/marcotcr/lime-experiments\n      + https://www.youtube.com/watch?v=bCgEP2zuYxI\n      + Introduces the LIME method (Local Interpretable Model-agnostic Explanations)\n\n    + (2016) A Model Explanation System: Latest Updates and Extensions by Ryan Turner\n      + https://arxiv.org/pdf/1606.09517\n      + http://www.blackboxworkshop.org/pdf/Turner2015_MES.pdf\n\n    + (2017) Understanding Black-box Predictions via Influence Functions by Pang Wei Koh, Percy Liang\n      + https://arxiv.org/pdf/1703.04730\n\n    + (2017) A Unified Approach to Interpreting Model Predictions by Scott Lundberg, Su-In Lee\n      + https://arxiv.org/pdf/1705.07874\n      + Code: https://github.com/slundberg/shap\n      + Introduces the SHAP method (SHapley Additive exPlanations), generalizing LIME\n\n    + (2018) Anchors: High-Precision Model-Agnostic Explanations by Marco Ribeiro, Sameer Singh, Carlos Guestrin\n      + https://homes.cs.washington.edu/~marcotcr/aaai18.pdf\n      + Code: https://github.com/marcotcr/anchor-experiments\n\n    + (2018) Learning to Explain: An Information-Theoretic Perspective on Model Interpretation by Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan\n      + https://arxiv.org/pdf/1802.07814\n\n    + (2018) Explanations of model predictions with live and breakDown packages by Mateusz Staniak, Przemyslaw Biecek\n      + https://arxiv.org/pdf/1804.01955\n      + Docs: https://mi2datalab.github.io/live/\n      + Code: https://github.com/MI2DataLab/live\n      + Docs: https://pbiecek.github.io/breakDown\n      + Code: https://github.com/pbiecek/breakDown\n\n    + (2018) A review book -  Interpretable Machine Learning. A Guide for Making Black Box\n      Models Explainable by Christoph Molnar\n\n      + https://christophm.github.io/interpretable-ml-book/\n    + (2018) Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead by Cynthia Rudin\n      + https://arxiv.org/pdf/1811.10154\n    + (2019) Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition by Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl\n      + https://arxiv.org/pdf/1904.03867\n\n*** Model Specific Explanations - Neural Networks\n    + (2013) Visualizing and Understanding Convolutional Networks by Matthew D Zeiler, Rob Fergus\n      + https://arxiv.org/pdf/1311.2901\n\n    + (2013) Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps by Karen Simonyan, Andrea Vedaldi, Andrew Zisserman\n      + https://arxiv.org/pdf/1312.6034\n\n    + (2015) Understanding Neural Networks Through Deep Visualization by Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, Hod Lipson\n      + https://arxiv.org/pdf/1506.06579\n      + https://github.com/yosinski/deep-visualization-toolbox\n\n    + (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\n      + https://arxiv.org/pdf/1610.02391\n\n    + (2016) Generating Visual Explanations by Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, Trevor Darrell\n      + https://arxiv.org/pdf/1603.08507\n\n    + (2016) Rationalizing Neural Predictions by Tao Lei, Regina Barzilay, Tommi Jaakkola\n      + https://arxiv.org/pdf/1606.04155\n      + https://people.csail.mit.edu/taolei/papers/emnlp16_rationale_slides.pdf\n      + Code: https://github.com/taolei87/rcnn/tree/master/code/rationale\n\n    + (2016) Gradients of Counterfactuals by Mukund Sundararajan, Ankur Taly, Qiqi Yan\n      + https://arxiv.org/pdf/1611.02639\n\n    + Pixel entropy can be used to detect relevant picture regions (for CovNets)\n      + See Visualization section and Fig. 5 of the paper\n        + (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\n          + https://arxiv.org/pdf/1703.07047\n\n    + (2017) SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability by Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein\n      + https://arxiv.org/pdf/1706.05806\n      + https://research.googleblog.com/2017/11/interpreting-deep-neural-networks-with.html\n\n    + (2017) Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks by Jose Oramas, Kaili Wang, Tinne Tuytelaars\n      + https://arxiv.org/pdf/1712.06302\n\n    + (2017) Axiomatic Attribution for Deep Networks by Mukund Sundararajan, Ankur Taly, Qiqi Yan\n      + https://arxiv.org/pdf/1703.01365\n      + Code: https://github.com/ankurtaly/Integrated-Gradients\n      + Proposes Integrated Gradients Method\n      + See also: Gradients of Counterfactuals https://arxiv.org/pdf/1611.02639.pdf\n\n    + (2017) Learning Important Features Through Propagating Activation Differences by Avanti Shrikumar, Peyton Greenside, Anshul Kundaje\n      + https://arxiv.org/pdf/1704.02685\n\n      + Proposes Deep Lift method\n\n      + Code: https://github.com/kundajelab/deeplift\n\n      + Videos: https://www.youtube.com/playlist?list=PLJLjQOkqSRTP3cLB2cOOi_bQFw6KPGKML\n\n    + (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\n      + https://arxiv.org/pdf/1711.0867\n      + Review of failures for methods extracting most important pixels for prediction\n\n    + (2018) Classifier-agnostic saliency map extraction by Konrad Zolna, Krzysztof J. Geras, Kyunghyun Cho\n      + https://arxiv.org/pdf/1805.08249\n      + Code: https://github.com/kondiz/casme\n\n    + (2018) A Benchmark for Interpretability Methods in Deep Neural Networks by Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim\n      + https://arxiv.org/pdf/1806.10758\n\n    + (2018) The Building Blocks of Interpretability by Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, Alexander Mordvintsev\n      + https://dx.doi.org/10.23915/distill.00010\n      + Has some embeded links to notebooks\n      + Uses Lucid library https://github.com/tensorflow/lucid\n\n    + (2018) Hierarchical interpretations for neural network predictions by Chandan Singh, W. James Murdoch, Bin Yu\n      + https://arxiv.org/pdf/1806.05337\n      + Code: https://github.com/csinva/hierarchical_dnn_interpretations\n\n    + (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\n      + https://arxiv.org/pdf/1808.04260\n      + Code: https://github.com/albermax/innvestigate\n\n    + (2018) YASENN: Explaining Neural Networks via Partitioning Activation Sequences by Yaroslav Zharov, Denis Korzhenkov, Pavel Shvechikov, Alexander Tuzhilin\n      + https://arxiv.org/pdf/1811.02783\n\n    + (2019) Attention is not Explanation by Sarthak Jain, Byron C. Wallace\n      + https://arxiv.org/pdf/1902.10186\n\n    + (2019) Attention Interpretability Across NLP Tasks by Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, Manaal Faruqui\n      + https://arxiv.org/pdf/1909.11218\n\n    + (2019) GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction by Thai Le, Suhang Wang, Dongwon Lee\n      + https://arxiv.org/pdf/1911.02042\n      + Code: https://github.com/lethaiq/GRACE_KDD20\n\n** Extracting Interpretable Models From Complex Ones\n\n   + (2017) Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples by Gail Weiss, Yoav Goldberg, Eran Yahav\n     + https://arxiv.org/pdf/1711.09576\n\n   + (2017) Distilling a Neural Network Into a Soft Decision Tree by Nicholas Frosst, Geoffrey Hinton\n     + https://arxiv.org/pdf/1711.09784\n\n   + (2017) Detecting Bias in Black-Box Models Using Transparent Model Distillation by Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou\n     + http://www.aies-conference.com/2018/contents/papers/main/AIES_2018_paper_96.pdf\n\n** Model Visualization\n   + Visualizing Statistical Models: Removing the blindfold\n     + http://had.co.nz/stat645/model-vis.pdf\n\n   + Partial dependence plots\n     + http://scikit-learn.org/stable/auto_examples/ensemble/plot_partial_dependence.html\n     + pdp: An R Package for Constructing Partial Dependence Plots\n       https://journal.r-project.org/archive/2017/RJ-2017-016/RJ-2017-016.pdf\n       https://cran.r-project.org/web/packages/pdp/index.html\n\n   + ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages\n     + https://journal.r-project.org/archive/2016-2/tang-horikoshi-li.pdf\n     + CRAN https://cran.r-project.org/web/packages/ggfortify/index.html\n\n   + RandomForestExplainer\n     + Master thesis https://rawgit.com/geneticsMiNIng/BlackBoxOpener/master/randomForestExplainer_Master_thesis.pdf\n     + R code\n       + CRAN https://cran.r-project.org/web/packages/randomForestExplainer/index.html\n       + Code: https://github.com/MI2DataLab/randomForestExplainer\n\n   + ggRandomForest\n     + Paper (vignette) https://github.com/ehrlinger/ggRandomForests/raw/master/vignettes/randomForestSRC-Survival.pdf\n     + R code\n       + CRAN https://cran.r-project.org/web/packages/ggRandomForests/index.html\n       + Code: https://github.com/ehrlinger/ggRandomForests\n\n** Selected Review Talks and Tutorials\n   + Tutorial on Interpretable machine learning at ICML 2017\n     + Slides: http://people.csail.mit.edu/beenkim/papers/BeenK_FinaleDV_ICML2017_tutorial.pdf\n\n   + P. Biecek, Show Me Your Model - Tools for Visualisation of Statistical Models\n     + 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\n\n   + S. Ritchie, Just-So Stories of AI\n     + Video: https://www.youtube.com/watch?v=DiWkKqZChF0\n     + Slides: https://speakerdeck.com/sritchie/just-so-stories-for-ai-explaining-black-box-predictions\n\n   + C. Jarmul, Towards Interpretable Accountable Models\n     + Video: https://www.youtube.com/watch?v=B3PtcF-6Dtc\n     + Slides: https://docs.google.com/presentation/d/e/2PACX-1vR05kpagAbL5qo1QThxwu44TI5SQAws_UFVg3nUAmKp39uNG0xdBjcMA-VyEeqZRGGQtt0CS5h2DMTS/embed?start=false\u0026loop=false\u0026delayms=3000\n\n   + I. Oszvald, Machine Learning Libraries You'd Wish You'd Known About\n     + A large part of the talk covers model explanation and visualization\n     + Video: https://www.youtube.com/watch?v=nDF7_8FOhpI\n     + Associated notebook on explaining regression predictions: https://github.com/ianozsvald/data_science_delivered/blob/master/ml_explain_regression_prediction.ipynb\n\n   + G. Varoquaux, Understanding and diagnosing your machine-learning models (covers PDP and Lime among others)\n     + Video: https://www.youtube.com/watch?v=kbj3llSbaVA\n     + Slides: http://gael-varoquaux.info/interpreting_ml_tuto/\n\n** Venues\n   + Interpretable ML Symposium (NIPS 2017) (contains links to *papers*, *slides* and *videos*)\n     + http://interpretable.ml/\n     + Debate, Interpretability is necessary in machine learning\n       + https://www.youtube.com/watch?v=2hW05ZfsUUo\n   + Workshop on Human Interpretability in Machine Learning (WHI), organised in conjunction with ICML\n     + 2018 (contains links to *papers* and *slides*)\n       + https://sites.google.com/view/whi2018\n       + Proceedings https://arxiv.org/html/1807.01308\n     + 2017 (contains links to *papers* and *slides*)\n       + https://sites.google.com/view/whi2017/home\n       + Proceedings https://arxiv.org/html/1708.02666\n     + 2016 (contains links to *papers*)\n       + https://sites.google.com/site/2016whi/\n       + Proceedings https://arxiv.org/html/1607.02531 or [[https://drive.google.com/open?id=0B9mGJ4F63iKGZWk0cXZraTNjRVU][here]]\n   + Analyzing and interpreting neural networks for NLP (BlackboxNLP), organised in conjunction with EMNLP\n     + 2019 (links below may get prefixed by 2019 later on)\n       + https://blackboxnlp.github.io/\n       + https://blackboxnlp.github.io/program.html\n       + Papers should be available on arXiv\n     + 2018\n       + https://blackboxnlp.github.io/2018\n       + https://blackboxnlp.github.io/program.html\n       + [[https://arxiv.org/search/advanced?advanced=\u0026terms-0-operator=AND\u0026terms-0-term=BlackboxNLP\u0026terms-0-field=comments\u0026terms-1-operator=OR\u0026terms-1-term=Analyzing+interpreting+neural+networks+NLP\u0026terms-1-field=comments\u0026classification-physics_archives=all\u0026date-filter_by=all_dates\u0026date-year=\u0026date-from_date=\u0026date-to_date=\u0026date-date_type=submitted_date\u0026abstracts=show\u0026size=200\u0026order=-announced_date_first][List of papers]]\n   + FAT/ML Fairness, Accountability, and Transparency in Machine Learning [[https://www.fatml.org/]]\n     + 2018\n       + https://www.fatml.org/schedule/2018\n     + 2017\n       + https://www.fatml.org/schedule/2017\n     + 2016\n       + https://www.fatml.org/schedule/2016\n     + 2016\n       + https://www.fatml.org/schedule/2016\n     + 2015\n       + https://www.fatml.org/schedule/2015\n     + 2014\n       + https://www.fatml.org/schedule/2014\n    + AAAI/ACM Annual Conferenceon AI, Ethics, and Society\n      + 2019 (links below may get prefixed by 2019 later on)\n        + http://www.aies-conference.com/accepted-papers/\n      + 2018\n        + http://www.aies-conference.com/2018/accepted-papers/\n        + http://www.aies-conference.com/2018/accepted-student-papers/\n** Software\n   Software related to papers is mentioned along with each publication.\n   Here only standalone software is included.\n\n   + DALEX - R package, Descriptive mAchine Learning EXplanations\n     + CRAN https://cran.r-project.org/web/packages/DALEX/DALEX.pdf\n     + Code: https://github.com/pbiecek/DALEX\n\n   + ELI5 - Python package dedicated to debugging machine learning classifiers\n     and explaining their predictions\n     + Code: https://github.com/TeamHG-Memex/eli5\n     + https://eli5.readthedocs.io/en/latest/\n\n   + forestmodel - R package visualizing coefficients of different models with the so called forest plot\n     + CRAN https://cran.r-project.org/web/packages/forestmodel/index.html\n     + Code: https://github.com/NikNakk/forestmodel\n\n   + fscaret - R package with automated Feature Selection from 'caret'\n     + CRAN https://cran.r-project.org/web/packages/fscaret/\n     + Tutorial: https://cran.r-project.org/web/packages/fscaret/vignettes/fscaret.pdf\n\n   + iml - R package for Interpretable Machine Learning\n     + CRAN https://cran.r-project.org/web/packages/iml/\n     + Code: https://github.com/christophM/iml\n     + Publication: http://joss.theoj.org/papers/10.21105/joss.00786\n\n   + interpret - Python package package for training interpretable models and explaining blackbox systems by Microsoft\n     + Code: https://github.com/microsoft/interpret\n\n   + lime - R package implementing LIME\n     + https://github.com/thomasp85/lime\n\n   + lofo-importance - Python package feature importance by Leave One Feature Out Importance method\n     + Code: https://github.com/aerdem4/lofo-importance\n\n   + Lucid - a collection of infrastructure and tools for research in neural network interpretability\n     + Code: https://github.com/tensorflow/lucid\n\n   + 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)\n     + CRAN https://cran.r-project.org/web/packages/praznik/index.html\n     + Code: https://notabug.org/mbq/praznik\n\n   + yellowbrick - Python package offering visual analysis and diagnostic tools to facilitate machine learning model selection\n     + Code: https://github.com/DistrictDataLabs/yellowbrick\n     + http://www.scikit-yb.org/en/latest/\n\n** Other Resources\n   + *Awesome* list of resources by Patrick Hall\n     + https://github.com/jphall663/awesome-machine-learning-interpretability\n   + *Awesome* XAI resources by Przemysław Biecek\n     + https://github.com/pbiecek/xai_resources\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flopusz%2Fawesome-interpretable-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flopusz%2Fawesome-interpretable-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flopusz%2Fawesome-interpretable-machine-learning/lists"}