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In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right.
Quota solutions op n-person games1
LS Shapley · 1953
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Multilinear extensions of games
Guillermo Owen · 1972
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Black-box testing: techniques for functional testing of software and systems
Boris Beizer · 1995
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The misunderstood limits of folk science: An illusion of explanatory depth
Leonid Rozenblit and Frank Keil · 2002
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko · 2014
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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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European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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The accuracy, fairness, and limits of predicting recidivism
Julia Dressel and Hany Farid · 2018
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Consistent individualized feature attribution for tree ensembles
Scott M Lundberg, Gabriel G Erion, and Su-In Lee · 2018
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Explanations of model predictions with live and breakdown packages
Mateusz Staniak and Przemyslaw Biecek · 2018
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A multidisciplinary survey and framework for design and evaluation of explainable ai systems
Sina Mohseni, Niloofar Zarei, and Eric D Ragan · 2018
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Towards robust interpretability with self-explaining neural networks
David Alvarez Melis and Tommi Jaakkola · 2018
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Model agnostic supervised local explanations
Gregory Plumb, Denali Molitor, and Ameet S Talwalkar · 2018
Cited alongside, same era.
The ethical algorithm: The science of socially aware algorithm design
Michael Kearns and Aaron Roth · 2019
Cited alongside, same era.
Faithful and customizable explanations of black box models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2019
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
Cited alongside, same era.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
Cited alongside, same era.
The many shapley values for model explanation
Mukund Sundararajan and Amir Najmi · 2020
Cited alongside, same era.
Feature relevance quantification in explainable ai: A causal problem
Dominik Janzing, Lenon Minorics, and Patrick Bl"̈obaum · 2020
Later among the works it cites.
"" how do i fool you?"" manipulating user trust via misleading black box explanations
Himabindu Lakkaraju and Osbert Bastani · 2020
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Explainable ai: Foundations, industrial applications, practical challenges, and lessons learned
Freddy Lecue, Krishna Gade, Sahin Geyik, Kenthapadi Krishnaram, Varun Mithal, Ankur Taly, Riccardo Guidotti, and Pasquale Minervini · 2020
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How can i explain this to you? an empirical study of deep neural network explanation methods
Jeya Vikranth Jeyakumar, Joseph Noor, Yu-Hsi Cheng, Luis Garcia, and Mani Srivastava · 2020
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The shapley taylor interaction index
Mukund Sundararajan, Kedar Dhamdhere, and Ashish Agarwal · 2020
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Interpreting interpretability: understanding data scientists’ use of interpretability tools for machine learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan · 2020
Cited alongside, same era.
Towards falsifiable interpretability research
Matthew L Leavitt and Ari Morcos · 2020
Cited alongside, same era.
Understanding global feature contributions with additive importance measures
Ian Covert, Scott M Lundberg, and Su-In Lee · 2020
Cited alongside, same era.
How does this interaction affect me? interpretable attribution for feature interactions
Michael Tsang, Sirisha Rambhatla, and Yan Liu · 2020
Cited alongside, same era.
Problems with shapley-value-based explanations as feature importance measures
I Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler · 2020
Cited alongside, same era.
Interpretable machine learning
Christoph Molnar · 2020
Cited alongside, same era.
I think i get your point, ai! the illusion of explanatory depth in explainable ai
Michael Chromik, Malin Eiband, Felicitas Buchner, Adrian Krüger, and Andreas Butz · 2021
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Interpretable artificial intelligence through the lens of feature interaction
Michael Tsang, James Enouen, and Yan Liu · 2021
Later among the works it cites.
Explainable artificial intelligence: an analytical review
Plamen P Angelov, Eduardo A Soares, Richard Jiang, Nicholas I Arnold, and Peter M Atkinson · 2021
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Svea: A small-scale benchmark for validating the usability of post-hoc explainable ai solutions in image and signal recognition
Sam Sattarzadeh, Mahesh Sudhakar, and Konstantinos N Plataniotis · 2021
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Synthetic benchmarks for scientific research in explainable machine learning
Yang Liu, Sujay Khandagale, Colin White, and Willie Neiswanger · 2021
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Two-way anova - the basics
Andreas Tilevik · 2021
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Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance
Giles Hooker, Lucas Mentch, and Siyu Zhou · 2021
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The shapley value in machine learning
Benedek Rozemberczki, Lauren Watson, Péter Bayer, Hao-Tsung Yang, Olivér Kiss, Sebastian Nilsson, and Rik Sarkar · 2022
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Accurate shapley values for explaining tree-based models
Salim I Amoukou, Tangi Salaün, and Nicolas Brunel · 2022
Closest in time.