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Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and security audits of ML models.
Fairwashing: the Risk of Rationalization
Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, and Alain Tapp · 1901
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Interpretable Machine Learning: Definitions, Methods, and Applications
W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 1901
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Assessing the Local Interpretability of Machine Learning Models
Sorelle A. Friedler, Chitradeep Dutta Roy, Carlos Scheidegger, and Dylan Slack · 1902
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SAFE ML: Surrogate Assisted Feature Extraction for Model Learning
Alicja Gosiewska, Aleksandra Gacek, Piotr Lubon, and Przemyslaw Biecek · 1902
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Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition
Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl · 1904
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Privacy Risks of Explaining Machine Learning Models
Reza Shokri, Martin Strobel, and Yair Zick · 1907
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How Can We Fool LIME and SHAP? Adversarial Attacks on Post-hoc Explanation Methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 1911
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The Shapley value: Essays in Honor of Lloyd S. Shapley
Lloyd S. Shapley, Alvin E. Roth, et al · 1988
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Extracting Tree-Structured Representations of Trained Networks
Mark W. Craven and Jude W. Shavlik · 1996
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Fraud Detection using Predictive Modeling, October 6 1998
Krishna M. Gopinathan, Louis S. Biafore, William M. Ferguson, Michael A. Lazarus, Anu K. Pathria, and Allen Jost · 1998
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The Elements of Statistical Learning
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
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Analysis of Regression in Game Theory Approach
Stan Lipovetsky and Michael Conklin · 2001
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Fair Attribution of Functional Contribution in Artificial and Biological Networks
Alon Keinan, Ben Sandbank, Claus C. Hilgetag, Isaac Meilijson, and Eytan Ruppin · 2004
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An Efficient Explanation of Individual Classifications using Game Theory
Erik Strumbelj and Igor Kononenko · 2010
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Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Data Preprocessing Techniques for Classification Without Discrimination
Faisal Kamiran and Toon Calders · 2012
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UCI Machine Learning Repository, 2013
M. Lichman · 2013
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Accurate Intelligible Models with Pairwise Interactions
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker · 2013
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Intriguing Properties of Neural Networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Modeltracker: Redesigning Performance Analysis Tools for Machine Learning
Saleema Amershi, Max Chickering, Steven M. Drucker, Bongshin Lee, Patrice Simard, and Jina Suh · 2015
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Certifying and Removing Disparate Impact
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Cited alongside, same era.
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin · 2015
Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Adrian Weller · 2017
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Interpretability
Mike Williams et al · 2017
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Hongyu Yang, Cynthia Rudin, and Margo Seltzer · 2017
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Towards Better Understanding of Gradient-based Attribution Methods for Deep Neural Networks
Marco Ancona, Enea Ceolini, Cengiz Oztireli, and Markus Gross · 2018
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Cited alongside, same era.
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Cited alongside, same era.
Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And It’s Biased Against Blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Cited alongside, same era.
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
Daniel W. Apley · 2016
Cited alongside, same era.
False Positives, False Negatives, and False Analyses: A Rejoinder to Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And It’s Biased against Blacks
Anthony W. Flores, Kristin Bechtel, and Christopher T. Lowenkamp · 2016
Cited alongside, same era.
Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
The Mythos of Model Interpretability
Zachary C. Lipton · 2016
Cited alongside, same era.
Why Should I Trust You?: Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Verifiable Reinforcement Learning Via Policy Extraction
Osbert Bastani, Yewen Pu, and Armando Solar-Lezama · 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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Locally Interpretable Models and Effects Based on Supervised Partitioning (LIME-SUP)
Linwei Hu, Jie Chen, Vijayan N. Nair, and Agus Sudjianto · 2018
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Debugging Machine Learning Models via Model Assertions, 2019
Daniel Kang, Deepti Raghavan, Peter Bailis, and Matei Zaharia · 2018
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Interpretable Machine Learning
Christoph Molnar · 2018
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Please Stop Explaining Black Box Models for High Stakes Decisions
Cynthia Rudin · 2018
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Explainable Neural Networks Based on Additive Index Models
Joel Vaughan, Agus Sudjianto, Erind Brahimi, Jie Chen, and Vijayan N. Nair · 2018
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Mitigating Unwanted Biases with Adversarial Learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
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On the Art and Science of Machine Learning Explanations
Patrick Hall · 2019
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