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We develop a method for generating causal post-hoc explanations of black-box classifiers based on a learned low-dimensional representation of the data.
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Diederik P. Kingma and Max Welling · 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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Deep convolutional inverse graphics network
Tejas Kulkarni, William Whitney, Pushmeet Kohli, and Josh Tenenbaum · 2015
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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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Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
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Variational Information Maximization for Feature Selection
Shuyang Gao, Greg Ver Steeg, and Aram Galstyan · 2016
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Peter Abbeel · 2016
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The Role of Explanation in Algorithmic Trust
Finale Doshi-Velez, Ryan Budish, and Mason Kortz · 2017
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Accountable Algorithms
Joshua Kroll, Joanna Huey, Solon Barocas, Edward Felten, Joel Reidenberg, David Robinson, and Harlan Yu · 2017
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Real Time Image Saliency for Black Box Classifiers
Piotr Dabkowski and Yarin Gal · 2017
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Interpretability via Model Extraction
Osbert Bastani, Carolyn Kim, and Hamsa Bastani · 2017
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Interpretable & Explorable Approximations of Black Box Models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Counterfactual Fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Avoiding Discrimination through Causal Reasoning
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi Jaakkola · 2017
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2017
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Beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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The Seven Tools of Causal Inference with Reflections on Machine Learning
Judea Pearl · 2019
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Explaining a black-box using deep variational information bottleneck approach
Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, and Eric Xing · 2019
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CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Patrick Schwab and Walter Karlen · 2019
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Learning Interpretable Models with Causal Guarantees
Carolyn Kim and Osbert Bastani · 2019
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Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
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Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation
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A Survey of Methods for Explaining Black Box Models
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Explaining deep learning models–a bayesian non-parametric approach
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Jorg Wagner, Jan Mathias Kohler, Tobias Gindele, Leon Hetzel, Jakob Thaddaus Wiedemer, and Sven Behnke · 2019
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Towards Providing Causal Explanations for the Predictions of any Classifier
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What made you do this? Understanding black-box decisions with sufficient input subsets
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Interpreting black box predictions using fisher kernels
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Explaining Visual Models by Causal Attribution
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Pc-fairness: A unified framework for measuring causality-based fairness
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Neural Network Attributions: A Causal Perspective
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Explaining Image Classifiers by Counterfactual Generation
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Learning to Explain With Complemental Examples
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Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
Raphael Suter, Dorde Miladinovic, Bernhard Schölkopf, and Stefan Bauer · 2019
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A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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G20 Ministerial Statement on Trade and Digital Economy
G20 · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, and Anna Goldenberg · 2019
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Causal Interpretability for Machine Learning – Problems, Methods and Evaluation
Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, and Huan Liu · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K. Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Interpretable Counterfactual Explanations Guided by Prototypes
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Explaining Classifiers with Causal Concept Effect (CaCE)
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New White House AI principles reach beyond economic and security considerations, Brookings Institution, January 2020
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