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Explainability is highly-desired in Machine Learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment.
Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, and David Madigan · 1932
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Trevor J Hastie and Robert J Tibshirani · 1990
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Extracting Rules from Trained Neural Networks
Hiroshi Tsukimoto · 2000
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Ambiguity and choice in public policy: Political decision making in modern democracies
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Peter Hase and Mohit Bansal · 2005
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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Leakage in data mining: Formulation, detection, and avoidance
Shachar Kaufman, Saharon Rosset, and Claudia Perlich · 2011
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Accuracy at the Top
Stephen Boyd, Corinna Cortes, Mehryar Mohri, and Ana Radovanovic · 2012
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Intelligible Models for Classification and Regression
Yin Lou, Rich Caruana, and Johannes Gehrke · 2012
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Accurate Intelligible Models with Pairwise Interactions
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker · 2013
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2013
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Supersparse Linear Integer Models for Interpretable Classification, jun 2013
Berk Ustun, Stefano Tracà, and Cynthia Rudin · 2013
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus Robert Müller, and Wojciech Samek · 2015
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Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Rich Caruana, Yin Lou, Johannes Gehrke Microsoft, Paul Koch, Marc Sturm, and Noémie Elhadad · 2015
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Identifying Police Officers at Risk of Adverse Events
Samuel Carton, Jennifer Helsby, Kenneth Joseph, Ayesha Mahmud, Youngsoo Park, Joe Walsh, Crystal Cody, CPT Estella Patterson, Lauren Haynes, and Rayid Ghani · 2016
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Examples are not Enough, Learn to Criticize! Criticism for Interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi Koyejo · 2016
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Interpretable Decision Sets: A Joint Framework for Description and Prediction
Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec · 2016
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Transductive Optimization of Top k Precision
Li-Ping Liu, Thomas G Dietterich, Nan Li, and Zhi-Hua Zhou · 2016
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The Politics of Evidence: From evidence-based policy to the good governance of evidence
Justin Parkhurst · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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What is wrong with evidence based policy, and how can it be improved?
Andrea Saltelli and Mario Giampietro · 2016
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Towards a rigorous science of interpretable machine learning, 2017
Finale Doshi-Velez and Been Kim · 2017
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Distilling a Neural Network Into a Soft Decision Tree
Nicholas Frosst and Geoffrey Hinton · 2017
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European Union regulations on algorithmic decision-making and a "right to explanation"
Bryce Goodman and Seth Flaxman · 2017
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Understanding Black-box Predictions via Influence Functions
Pang Wei Koh and Percy Liang · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Learning Important Features Through Propagating Activation Differences, 2017
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Scalable bayesian rule lists
Hongyu Yang, Cynthia Rudin, and Margo Seltzer · 2017
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Interpretable classification models for recidivism prediction
Jiaming Zeng, Berk Ustun, and Cynthia Rudin · 2017
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Deploying machine learning models for public policy: A framework
Klaus Ackermann, Hareem Naveed, Jason Bennett, Joe Walsh, Andrea Navarrete Rivera, Michael Defoe, Adolfo De Unánue, Sun Joo Lee, Crystal Cody, Lauren Haynes, and Rayid Ghani · 2018
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi and Mohammed Berrada · 2018
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Proxy tasks and subjective measures can be misleading in evaluating explainable ai systems
Zana Buçinca, Phoebe Lin, Krzysztof Z. Gajos, and Elena L. Glassman · 2020
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Explaining machine learning reveals policy challenges
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From local explanations to global understanding with explainable AI for trees
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Validation of a machine learning model to predict childhood lead poisoning
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Matthew J. Bauman, Erika Salomon, Joe Walsh, Robert Sullivan, Kate S. Boxer, Hareem Naveed, Jen Helsby, Chris Schneweis, Tzu Yun Lin, Lauren Haynes, Steve Yoder, and Rayid Ghani · 2018
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A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
Alexandra Chouldechova, Emily Putnam-Hornstein, Suzanne Dworak-Peck, Diana Benavides-Prado, Oleksandr Fialko, Rhema Vaithianathan, Sorelle A Friedler, and Christo Wilson · 2018
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Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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The mythos of model interpretability
Zachary C. Lipton · 2018
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Model Agnostic Supervised Local Explanations
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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