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A multitude of explainability methods and associated fidelity performance metrics have been proposed to help better understand how modern AI systems make decisions.
“Comparing individual means in the analysis of variance”
John Tukey · 1949
Earlier work this paper cites.
“The similarity metric”
Ming Li et al · 2004
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“Models of bottom-up attention and saliency”
L. Itti · 2005
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“Functional explanation and the function of explanation”
Tania Lombrozo and Susan Carey · 2006
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“Approximating rate-distortion graphs of individual data: Experiments in lossy compression and denoising”
Steven de Rooij and Paul Vitányi · 2006
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“Confounds in pictorial sets: The role of complexity and familiarity in basic-level picture processing”
Alex Forsythe, Gerry Mulhern and Martin Sawey · 2008
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“ImageNet: A Large-Scale Hierarchical Image Database”
J. Deng et al · 2009
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“Visual complexity: is that all there is?”
Alexandra Forsythe · 2009
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“The role of explanation in discovery and generalization: Evidence from category learning”
Joseph Williams and Tania Lombrozo · 2010
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“Are your participants gaming the system? Screening Mechanical Turk workers”
Julie Downs, Mandy Holbrook, Steve Sheng and Lorrie Cranor · 2010
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“The instrumental value of explanations”
Tania Lombrozo · 2011
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“Image complexity measure based on visual attention”
Matthieur Da, Vincent Courboulay and Pascal Estraillier · 2011
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“Deep inside convolutional networks: Visualising image classification models and saliency maps”
Karen Simonyan, Andrea Vedaldi and Andrew Zisserman · 2014
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“Visualizing and understanding convolutional networks”
Matthew Zeiler and Rob Fergus · 2014
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“Explanation and inference: Mechanistic and functional explanations guide property generalization”
Tania Lombrozo and Nicholas Gwynne · 2014
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“Evaluating the visualization of what a Deep Neural Network has learned”
Wojciech Samek et al · 2015
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“Going deeper with convolutions”
Christian Szegedy et al · 2015
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“"Why Should I Trust You?": Explaining the Predictions of Any Classifier”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2016
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“The Mythos of Model Interpretability”
Zachary. Lipton · 2016
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“Explanatory preferences shape learning and inference”
Tania Lombrozo · 2016
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“Machine Bias”, 2016
Julia Angwin, Jeff Larson, Surya Mattu and ProPublica Kirchner Lauren · 2016
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“Computer vision cracks the leaf code”
Peter Wilf et al · 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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“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“Axiomatic Attribution for Deep Networks”
Mukund Sundararajan, Ankur Taly and Qiqi Yan · 2017
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“SmoothGrad: removing noise by adding noise”
Daniel Smilkov et al · 2017
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“Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization”
Ramprasaath. Selvaraju et al · 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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“Interpretable Explanations of Black Boxes by Meaningful Perturbation”
Ruth. Fong and Andrea Vedaldi · 2017
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“Towards a rigorous science of interpretable machine learning”
Finale Doshi-Velez and Been Kim · 2017
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“Mastering the game of go without human knowledge”
David Silver et al · 2017
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“Explaining recurrent neural network predictions in sentiment analysis”
Leila Arras, Grégoire Montavon, Klaus-Robert Müller and Wojciech Samek · 2017
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“" What is relevant in a text document?": An interpretable machine learning approach”
Leila Arras et al · 2017
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“Learning Important Features Through Propagating Activation Differences”
Peyton Avanti and Anshul Kundaje · 2017
Cited alongside, same era.
“Learning Important Features Through Propagating Activation Differences”
Avanti Shrikumar, Peyton Greenside and Anshul Kundaje · 2017
“Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?”
Alon Jacovi and Yoav Goldberg · 2020
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“Evaluating and Aggregating Feature-based Model Explanations”
Umang Bhatt, Adrian Weller and José.. Moura · 2020
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“White Paper Machine Learning in Certified Systems”, 2021
Franck Mamalet et al · 2021
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“Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis”
Fel Thomas et al · 2021
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“Explainability of vision-based autonomous driving systems: Review and challenges”
Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez and Matthieu Cord · 2021
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“Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball”
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“Rise: Randomized input sampling for explanation of black-box models”
Vitali Petsiuk, Abir Das and Kate Saenko · 2018
Cited alongside, same era.
“Learning what and where to attend”
Drew Linsley, Dan Shiebler, Sven Eberhardt and Thomas Serre · 2018
Cited alongside, same era.
“Towards better understanding of gradient-based attribution methods for Deep Neural Networks”
Marco Ancona, Enea Ceolini, Cengiz Öztireli and Markus Gross · 2018
Cited alongside, same era.
“Top-down neural attention by excitation backprop”
Jianming Zhang et al · 2018
Cited alongside, same era.
“Teaching categories to human learners with visual explanations”
Oisin Mac et al · 2018
Cited alongside, same era.
“Do explanations make VQA models more predictable to a human?”
Arjun Chandrasekaran et al · 2018
Cited alongside, same era.
Andrew Elliott, Stephen Law and Chris Russell · 2021
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“The effectiveness of feature attribution methods and its correlation with automatic evaluation scores”
Giang Nguyen, Daeyoung Kim and Anh Nguyen · 2021
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“Acquisition of Chess Knowledge in AlphaZero”
Thomas McGrath et al · 2021
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“Highly accurate protein structure prediction with AlphaFold”
John Jumper et al · 2021
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“Advancing mathematics by guiding human intuition with AI”
Alex Davies et al · 2021
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“Does Explainable Artificial Intelligence Improve Human Decision-Making?”
Yasmeen Alufaisan et al · 2021
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“Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated”
Felix Biessmann and Dionysius Refiano · 2021
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“Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball”
Andrew Elliott, Stephen Law and Chris Russell · 2021
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“Exploring Alignment of Representations with Human Perception”
Vedant Nanda et al · 2021
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“Deceptive learning in histopathology”
Sahar Shahamatdar et al · 2022
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“Harmonizing the object recognition strategies of deep neural networks with humans”
Thomas Fel, Ivan Felipe, Drew Linsley and Thomas Serre · 2022
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“Don’t Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis”
Thomas Fel et al · 2022
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“Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure”
Paul Novello, Thomas Fel and David Vigouroux · 2022
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“Representativity and Consistency Measures for Deep Neural Network Explanations”
Thomas Fel and David Vigouroux · 2022
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“When and how children use explanations to guide generalizations”
Ny Vasil, Azzurra Ruggeri and Tania Lombrozo · 2022
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“Motivated to learn: An account of explanatory satisfaction”
Emily Liquin and Tania Lombrozo · 2022
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“Ethical AI in facial expression analysis: racial bias”
Abdallah Sham et al · 2022
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“Magnetic control of tokamak plasmas through deep reinforcement learning”
Jonas Degrave et al · 2022
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“Decoding family-level features for modern and fossil leaves from computer-vision heat maps”
Edward Spagnuolo, Peter Wilf and Thomas Serre · 2022
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“Visual correspondence-based explanations improve AI robustness and human-AI team accuracy”
Giang Nguyen, Mohammad Taesiri and Anh Nguyen · 2022
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“HIVE: Evaluating the Human Interpretability of Visual Explanations”
Sunnie.. Kim et al · 2022
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“Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset”
Leon Sixt et al · 2022
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“Xplique: A Deep Learning Explainability Toolbox”
Thomas Fel et al · 2022
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