ViCE: Visual Counterfactual Explanations for Machine Learning Models

ViCE: Visual Counterfactual Explanations for Machine Learning Models

5 March 2020

Papers citing "ViCE: Visual Counterfactual Explanations for Machine Learning Models"

15 / 15 papers shown

Title
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 61 487 0 09 Jul 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 98 1,005 0 19 May 2019
Actionable Recourse in Linear Classification Berk Ustun Alexander Spangher Yang Liu FaML 84 545 0 18 Sep 2018
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models Jiawei Zhang Yang Wang Piero Molino Lezhi Li D. Ebert FAtt 19 203 0 01 Aug 2018
RuleMatrix: Visualizing and Understanding Classifiers with Rules Yao Ming Huamin Qu E. Bertini FAtt 34 215 0 17 Jul 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 57 693 0 21 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 69 3,922 0 06 Feb 2018
Inverse Classification for Comparison-based Interpretability in Machine Learning Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 102 100 0 22 Dec 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 48 2,332 0 01 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 210 4,229 0 22 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 278 21,459 0 22 May 2017
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations Josua Krause Aritra Dasgupta Jordan Swartz Yindalon Aphinyanagphongs E. Bertini FAtt 28 96 0 04 May 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 334 3,742 0 28 Feb 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 78 3,672 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 340 16,765 0 16 Feb 2016

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