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While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods.
Using the adap learning algorithm to forecast the onset of diabetes mellitus
Jack W Smith, James E Everhart, WC Dickson, William C Knowler, and Robert Scott Johannes · 1988
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Algorithmic recourse: from counterfactual explanations to interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 2002
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Classification by set cover: The prototype vector machine
Jacob Bien and Robert Tibshirani · 2009
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che-hui Lien · 2009
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Intelligible models for classification and regression
Yin Lou, Rich Caruana, and Johannes Gehrke · 2012
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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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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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The effect of race/ethnicity on sentencing: Examining sentence type, jail length, and prison length
Kareem L Jordan and Tina L Freiburger · 2015
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Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H McCormick, and David Madigan · 2015
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Falling rule lists
Fulton Wang and Cynthia Rudin · 2015
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Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec · 2016
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The mythos of model interpretability
Zachary C Lipton · 2016
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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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Mapping chemical performance on molecular structures using locally interpretable explanations
Leanne S Whitmore, Anthe George, and Corey M Hudson · 2016
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Interpretability via model extraction
Osbert Bastani, Carolyn Kim, and Hamsa Bastani · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Earlier work this paper cites.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Interpretable classification models for recidivism prediction
Jiaming Zeng, Berk Ustun, and Cynthia Rudin · 2017
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On the robustness of interpretability methods
David Alvarez-Melis and Tommi S Jaakkola · 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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Evaluating feature importance estimates
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2018
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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.
Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach · 2018
Cited alongside, same era.
Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Cited alongside, same era.
Fairwashing: the risk of rationalization
Ulrich Aivodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, and Alain Tapp · 2019
Cited alongside, same era.
On the interpretability of machine learning-based model for predicting hypertension
Radwa Elshawi, Mouaz H Al-Mallah, and Sherif Sakr · 2019
Cited alongside, same era.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2021
Later among the works it cites.
Explaining by removing: A unified framework for model explanation
Ian Covert, Scott Lundberg, and Su-In Lee · 2021
Later among the works it cites.
When comparing to ground truth is wrong: On evaluating gnn explanation methods
Lukas Faber, Amin K. Moghaddam, and Roger Wattenhofer · 2021
Later among the works it cites.
The false hope of current approaches to explainable artificial intelligence in health care
Marzyeh Ghassemi, Luke Oakden-Rayner, and Andrew L Beam · 2021
Later among the works it cites.
How can i choose an explainer? an application-grounded evaluation of post-hoc explanations
Sérgio Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, and João Gama · 2021
Later among the works it cites.
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Cited alongside, same era.
Global explanations of neural networks: Mapping the landscape of predictions
Mark Ibrahim, Melissa Louie, Ceena Modarres, and John Paisley · 2019
Cited alongside, same era.
Model-agnostic counterfactual explanations for consequential decisions
Amir-Hossein Karimi, Gilles Barthe, Borja Balle, and Isabel Valera · 2019
Cited alongside, same era.
An evaluation of the human-interpretability of explanation
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, and Finale Doshi-Velez · 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.
Interpretable counterfactual explanations guided by prototypes
Arnaud Looveren and Janis Klaise · 2019
Cited alongside, same era.
Definitions, methods, and applications in interpretable machine learning
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
Cited alongside, same era.
Joon Sik Kim, Gregory Plumb, and Ameet Talwalkar · 2021
Later among the works it cites.
Explainable ai: A review of machine learning interpretability methods
Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis · 2021
Later among the works it cites.
Synthetic benchmarks for scientific research in explainable machine learning
Yang Liu, Sujay Khandagale, Colin White, and Willie Neiswanger · 2021
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Carla: A python library to benchmark algorithmic recourse and counterfactual explanation algorithms
Martin Pawelczyk, Sascha Bielawski, Johan Van den Heuvel, Tobias Richter, and Gjergji Kasneci · 2021
Later among the works it cites.
Reliable post hoc explanations: Modeling uncertainty in explainability
Dylan Slack, Anna Hilgard, Sameer Singh, and Himabindu Lakkaraju · 2021
Later among the works it cites.
Towards robust and reliable algorithmic recourse
Sohini Upadhyay, Shalmali Joshi, and Himabindu Lakkaraju · 2021
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Evaluating the quality of machine learning explanations: A survey on methods and metrics
Jianlong Zhou, Amir H Gandomi, Fang Chen, and Andreas Holzinger · 2021
Later among the works it cites.
https://www.kaggle.com/datasets/aasheesh200/framingham-heart-study-dataset
Framingham heart study dataset | kaggle · 2022
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Rethinking stability for attribution-based explanations
Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, and Himabindu Lakkaraju · 2022
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The road to explainability is paved with bias: Measuring the fairness of explanations
Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, and Marzyeh Ghassemi · 2022
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Use-case-grounded simulations for explanation evaluation
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, and Ameet Talwalkar · 2022
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Fairness via explanation quality: Evaluating disparities in the quality of post hoc explanations
Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen H Bach, and Himabindu Lakkaraju · 2022
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Framework for evaluating faithfulness of local explanations
Sanjoy Dasgupta, Nave Frost, and Michal Moshkovitz · 2022
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On the adversarial robustness of causal algorithmic recourse
Ricardo Dominguez-Olmedo, Amir H Karimi, and Bernhard Schölkopf · 2022
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Explainable machine learning challenge
FICO · 2022
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Attribution-based explanations that provide recourse cannot be robust
Hidde Fokkema, Rianne de Heide, and Tim van Erven · 2022
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Give me some credit :: 2011 competition data | kaggle
Bryce Freshcorn · 2022
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Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations
Tessa Han, Suraj Srinivas, and Himabindu Lakkaraju · 2022
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Quantus: an explainable ai toolkit for responsible evaluation of neural network explanations
Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M-C Höhne · 2022
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The disagreement problem in explainable machine learning: A practitioner’s perspective
Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju · 2022
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