The Challenge of Crafting Intelligible Intelligence

9 March 2018

Papers citing "The Challenge of Crafting Intelligible Intelligence"

24 / 24 papers shown

Title
Grounding Visual Explanations Lisa Anne Hendricks Ronghang Hu Trevor Darrell Zeynep Akata FAtt 53 226 0 25 Jul 2018
Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations S. Sreedharan Siddharth Srivastava S. Kambhampati 55 19 0 19 Feb 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 205 1,837 0 30 Nov 2017
Grounding Visual Explanations (Extended Abstract) Lisa Anne Hendricks Ronghang Hu Trevor Darrell Zeynep Akata FAtt 42 3 0 17 Nov 2017
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation Tarek R. Besold Artur Garcez Sebastian Bader Howard L. Bowman Pedro M. Domingos ... P. Lima L. Penning Gadi Pinkas Hoifung Poon Gerson Zaverucha LRM AI4CE 68 343 0 10 Nov 2017
Explainable Planning M. Fox D. Long Daniele Magazzeni 60 287 0 29 Sep 2017
Machine Teaching: A New Paradigm for Building Machine Learning Systems Patrice Y. Simard Saleema Amershi D. M. Chickering Alicia Edelman Pelton S. Ghorashi ... Gonzalo A. Ramos Jina Suh J. Verwey Mo Wang J. Wernsing HAI 52 179 0 21 Jul 2017
Interpretable & Explorable Approximations of Black Box Models Himabindu Lakkaraju Ece Kamar R. Caruana J. Leskovec FAtt 65 254 0 04 Jul 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 239 4,249 0 22 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.0K 21,815 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 50 96 0 04 May 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 138 1,514 1 19 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 169 2,878 0 14 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 376 3,776 0 28 Feb 2017
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 211 4,301 0 07 Oct 2016
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 63 1,897 0 28 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 170 3,685 0 10 Jun 2016
Generating Visual Explanations Lisa Anne Hendricks Zeynep Akata Marcus Rohrbach Jeff Donahue Bernt Schiele Trevor Darrell VLM FAtt 81 618 0 28 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.1K 16,931 0 16 Feb 2016
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 821 27,303 0 02 Dec 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 245 19,017 0 20 Dec 2014
Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau Kyunghyun Cho Yoshua Bengio AIMat 517 27,263 0 01 Sep 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 299 7,289 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 552 15,874 0 12 Nov 2013