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There have been several research works proposing new Explainable AI (XAI) methods designed to generate model explanations having specific properties, or desiderata, such as fidelity, robustness, or human-interpretability.
Sulla determinazione empirica di una lgge di distribuzione
Andrey Kolmogorov · 1933
Earlier work this paper cites.
On a test of whether one of two random variables is stochastically larger than the other
H. B. Mann and D. R. Whitney · 1947
Earlier work this paper cites.
Use of ranks in one-criterion variance analysis
William H. Kruskal and W. Allen Wallis · 1952
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The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability
Joseph L. Fleiss and Jacob Cohen · 1973
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A simple sequentially rejective multiple test procedure
Sture Holm · 1979
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A simple sequentially rejective multiple test procedure
Sture Holm · 1979
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A simple, fast, and effective rule learner
William W. Cohen and Yoram Singer · 1999
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
Predictive learning via rule ensembles
Jerome H. Friedman and Bogdan E. Popescu · 2008
Earlier work this paper cites.
Maximum likelihood rule ensembles
Krzysztof Dembczynski, Wojciech Kotlowski, and Roman Slowinski · 2008
Earlier work this paper cites.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Paul Koch, Yin Lou, Marc Sturm , Johannes Gehrke, and Noemie Elhadad · 2015
Earlier work this paper cites.
Towards extracting faithful and descriptive representations of latent variable models
Ivan Sanchez, Tim Rocktaschel, Sebastian Riedel, and Sameer Singh · 2015
Earlier work this paper cites.
treeinterpreter, 2015
Ando Saabas · 2015
Earlier work this paper cites.
Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46
General Data Protection Regulation · 2016
Earlier work this paper cites.
"why should i trust you?": Explaining the predictions of any classifier, 2016
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
"why should i trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec · 2016
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning, 2017
Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
The mythos of model interpretability
Zachary C Lipton · 2018
Later among the works it cites.
Distill-and-compare: Auditing black-box models using transparent model distillation
Sarah Tan, Rich Caruana, Giles Hooker, and Yin Lou · 2018
Later among the works it cites.
Towards robust interpretability with self-explaining neural networks
David Alvarez Melis and Tommi Jaakkola · 2018
Later among the works it cites.
On the robustness of interpretability methods
David Alvarez-Melis and Tommi S. Jaakkola · 2018
Later among the works it cites.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Jun Cai, James Wexler, Fernanda Viegas, and Rory Abbott Sayres · 2018
Later among the works it cites.
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Tim Miller, Piers Howe, and Liz Sonenberg · 2017
Cited alongside, same era.
The doctor just won’t accept that!
Zachary C Lipton · 2017
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
Interpretable to whom? a role-based model for analyzing interpretable machine learning systems
Richard Tomsett, Dave Braines, Dan Harborne, Alun Preece, and Supriyo Chakraborty · 2018
Cited alongside, same era.
A multidisciplinary survey and framework for design and evaluation of explainable ai systems
Sina Mohseni, Niloofar Zarei, and Eric D Ragan · 2018
Cited alongside, same era.
Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Cited alongside, same era.
Consistent individualized feature attribution for tree ensembles
Scott M Lundberg, Gabriel G Erion, and Su-In Lee · 2018
Cited alongside, same era.
Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, and Rich Caruana · 2019
Later among the works it cites.
Globally-consistent rule-based summary-explanations for machine learning models: Application to credit-risk evaluation
Cynthia Rudin and Yaron Shaposhnik · 2019
Later among the works it cites.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Y. Zou · 2019
Later among the works it cites.
Definitions, methods, and applications in interpretable machine learning
W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
Later among the works it cites.
Quantifying interpretability and trust in machine learning systems
Philipp Schmidt and Felix Biessmann · 2019
Later among the works it cites.
Case-based reasoning for assisting domain experts in processing fraud alerts of black-box machine learning models, 2019
Hilde J. P. Weerts, Werner van Ipenburg, and Mykola Pechenizkiy · 2019
Later among the works it cites.
What can ai do for me? evaluating machine learning interpretations in cooperative play
Shi Feng and Jordan Boyd-Graber · 2019
Later among the works it cites.
On human predictions with explanations and predictions of machine learning models: A case study on deception detection
Vivian Lai and Chenhao Tan · 2019
Later among the works it cites.
Explainable machine learning for public policy: Use cases, gaps, and research directions
Kasun Amarasinghe, Kit Rodolfa, Hemank Lamba, and Rayid Ghani · 2020
Later among the works it cites.
From local explanations to global understanding with explainable ai for trees
Scott Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee · 2020
Later among the works it cites.