Do Not Trust Additive Explanations

27 March 2019

Papers citing "Do Not Trust Additive Explanations"

17 / 17 papers shown

Title
shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python Martin Jullum Lars Henry Berge Olsen Jon Lachmann Annabelle Redelmeier TDI FAtt 111 2 0 02 Apr 2025
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Himabindu Lakkaraju Osbert Bastani 46 252 0 15 Nov 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 58 449 0 27 Jan 2019
Understanding Convolutional Neural Networks for Text Classification Alon Jacovi Oren Sar Shalom Yoav Goldberg FAtt 44 218 0 21 Sep 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 52 524 0 21 Jun 2018
Explanations of model predictions with live and breakDown packages M. Staniak P. Biecek FAtt 11 117 0 05 Apr 2018
Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"? L. Edwards Michael Veale FaML AILaw 32 135 0 20 Mar 2018
Consistent Individualized Feature Attribution for Tree Ensembles Scott M. Lundberg G. Erion Su-In Lee FAtt TDI 52 1,379 0 12 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 81 2,332 0 01 Nov 2017
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 295 204 0 06 Jul 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 549 21,613 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 123 3,848 0 10 Apr 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 216 19,796 0 07 Oct 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 587 16,828 0 16 Feb 2016
Adverse Events in Robotic Surgery: A Retrospective Study of 14 Years of FDA Data H. Alemzadeh Ravishankar Iyer Zbigniew T. Kalbarczyk N. Leveson J. Raman 29 303 0 13 Jul 2015
OpenML: networked science in machine learning Joaquin Vanschoren Jan N. van Rijn B. Bischl Luís Torgo FedML AI4CE 100 1,310 0 29 Jul 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 194 7,252 0 20 Dec 2013