Contrastive Explanations with Local Foil Trees

19 June 2018

J. V. D. Waa

M. Robeer

J. Diggelen

Matthieu J. S. Brinkhuis

Mark Antonius Neerincx

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Papers citing "Contrastive Explanations with Local Foil Trees"

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Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills Zana Buçinca S. Swaroop Amanda E. Paluch Finale Doshi-Velez Krzysztof Z. Gajos 69 2 0 05 Oct 2024
SurvLIME: A method for explaining machine learning survival models M. Kovalev Lev V. Utkin E. Kasimov 169 90 0 18 Mar 2020
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Pai-Shun Ting Karthikeyan Shanmugam Payel Das FAtt 89 587 0 21 Feb 2018
A comparative study of fairness-enhancing interventions in machine learning Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian Sonam Choudhary Evan P. Hamilton Derek Roth FaML 88 639 0 13 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 81 3,922 0 06 Feb 2018
Interpretable Policies for Reinforcement Learning by Genetic Programming D. Hein Steffen Udluft Thomas Runkler OffRL 32 131 0 12 Dec 2017
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences Tim Miller Piers Howe L. Sonenberg AI4TS SyDa 43 373 0 02 Dec 2017
The Promise and Peril of Human Evaluation for Model Interpretability Bernease Herman 42 144 0 20 Nov 2017
MAGIX: Model Agnostic Globally Interpretable Explanations Nikaash Puri Piyush B. Gupta Pratiksha Agarwal Sukriti Verma Balaji Krishnamurthy FAtt 86 41 0 22 Jun 2017
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly Ethan R. Elenberg A. Dimakis Moran Feldman Amin Karbasi 34 89 0 08 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 115 5,920 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 346 3,742 0 28 Feb 2017
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations Upol Ehsan Brent Harrison Larry Chan Mark O. Riedl 101 218 0 25 Feb 2017
An unexpected unity among methods for interpreting model predictions Scott M. Lundberg Su-In Lee FAtt 42 110 0 22 Nov 2016
TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning Jayaraman J. Thiagarajan B. Kailkhura P. Sattigeri Karthikeyan N. Ramamurthy 45 38 0 22 Nov 2016
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 209 19,796 0 07 Oct 2016
Semantics derived automatically from language corpora contain human-like biases Aylin Caliskan J. Bryson Arvind Narayanan 133 2,650 0 25 Aug 2016
Top-down Neural Attention by Excitation Backprop Jianming Zhang Zhe Lin Jonathan Brandt Xiaohui Shen Stan Sclaroff 53 946 0 01 Aug 2016
Rationalizing Neural Predictions Tao Lei Regina Barzilay Tommi Jaakkola 84 809 0 13 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 123 3,672 0 10 Jun 2016
The Latin American Giant Observatory: a successful collaboration in Latin America based on Cosmic Rays and computer science domains Hernán Asorey R. Mayo-García L. Núñez M. Pascual A. J. Rubio-Montero M. Suárez-Durán L. A. Torres-Niño 63 690 0 30 May 2016
Generating Visual Explanations Lisa Anne Hendricks Zeynep Akata Marcus Rohrbach Jeff Donahue Bernt Schiele Trevor Darrell VLM FAtt 65 620 0 28 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 577 16,828 0 16 Feb 2016
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition G. Montavon Sebastian Lapuschkin Alexander Binder Wojciech Samek Klaus-Robert Muller FAtt 53 730 0 08 Dec 2015