Towards A Rigorous Science of Interpretable Machine Learning

28 February 2017

Finale Doshi-Velez

Papers citing "Towards A Rigorous Science of Interpretable Machine Learning"

50 / 403 papers shown

Title
Artificial Intelligence for Pediatric Ophthalmology J. Reid Eric Eaton 16 60 0 06 Apr 2019
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations Fred Hohman Haekyu Park Caleb Robinson Duen Horng Chau FAtt 3DH HAI 11 213 0 04 Apr 2019
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 11 21 0 01 Apr 2019
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems Laura von Rueden S. Mayer Katharina Beckh B. Georgiev Sven Giesselbach ... Rajkumar Ramamurthy Michal Walczak Jochen Garcke Christian Bauckhage Jannis Schuecker 34 626 0 29 Mar 2019
A Grounded Interaction Protocol for Explainable Artificial Intelligence Prashan Madumal Tim Miller L. Sonenberg F. Vetere 14 96 0 05 Mar 2019
Using Causal Analysis to Learn Specifications from Task Demonstrations Daniel Angelov Yordan V. Hristov S. Ramamoorthy CML 21 19 0 04 Mar 2019
Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability Brian Lubars Chenhao Tan 14 73 0 08 Feb 2019
Towards Automatic Concept-based Explanations Amirata Ghorbani James Wexler James Zou Been Kim FAtt LRM 22 19 0 07 Feb 2019
Fairwashing: the risk of rationalization Ulrich Aivodji Hiromi Arai O. Fortineau Sébastien Gambs Satoshi Hara Alain Tapp FaML 17 142 0 28 Jan 2019
Explaining Explanations to Society Leilani H. Gilpin Cecilia Testart Nathaniel Fruchter Julius Adebayo XAI 19 34 0 19 Jan 2019
Optimization Problems for Machine Learning: A Survey Claudio Gambella Bissan Ghaddar Joe Naoum-Sawaya AI4CE 28 178 0 16 Jan 2019
A multi-task deep learning model for the classification of Age-related Macular Degeneration Qingyu Chen Yifan Peng T. Keenan S. Dharssi Elvira Agrón W. Wong E. Chew Zhiyong Lu BDL MedIm 21 45 0 02 Dec 2018
Image Reconstruction with Predictive Filter Flow Shu Kong Charless C. Fowlkes SupR 16 13 0 28 Nov 2018
Scalable agent alignment via reward modeling: a research direction Jan Leike David M. Krueger Tom Everitt Miljan Martic Vishal Maini Shane Legg 28 392 0 19 Nov 2018
Secure Deep Learning Engineering: A Software Quality Assurance Perspective L. Ma Felix Juefei Xu Minhui Xue Q. Hu Sen Chen Bo-wen Li Yang Liu Jianjun Zhao Jianxiong Yin Simon See AAML 17 35 0 10 Oct 2018
What made you do this? Understanding black-box decisions with sufficient input subsets Brandon Carter Jonas W. Mueller Siddhartha Jain David K Gifford FAtt 35 77 0 09 Oct 2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 11 128 0 08 Oct 2018
Interpreting Layered Neural Networks via Hierarchical Modular Representation C. Watanabe 8 19 0 03 Oct 2018
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts Samuel Carton Qiaozhu Mei Paul Resnick FAtt AAML 9 34 0 01 Sep 2018
An Operation Sequence Model for Explainable Neural Machine Translation Felix Stahlberg Danielle Saunders Bill Byrne LRM MILM 25 29 0 29 Aug 2018
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 22 1,071 0 31 Jul 2018
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach Yeounoh Chung Tim Kraska N. Polyzotis Ki Hyun Tae Steven Euijong Whang 17 129 0 16 Jul 2018
Model Agnostic Supervised Local Explanations Gregory Plumb Denali Molitor Ameet Talwalkar FAtt LRM MILM 14 196 0 09 Jul 2018
xGEMs: Generating Examplars to Explain Black-Box Models Shalmali Joshi Oluwasanmi Koyejo Been Kim Joydeep Ghosh MLAU 17 40 0 22 Jun 2018
Learning Qualitatively Diverse and Interpretable Rules for Classification A. Ross Weiwei Pan Finale Doshi-Velez 16 13 0 22 Jun 2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems Richard J. Tomsett Dave Braines Daniel Harborne Alun D. Preece Supriyo Chakraborty FaML 21 164 0 20 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 13 932 0 20 Jun 2018
Contrastive Explanations with Local Foil Trees J. V. D. Waa M. Robeer J. Diggelen Matthieu J. S. Brinkhuis Mark Antonius Neerincx FAtt 14 82 0 19 Jun 2018
Instance-Level Explanations for Fraud Detection: A Case Study Dennis Collaris L. M. Vink J. V. Wijk 29 31 0 19 Jun 2018
Learning Kolmogorov Models for Binary Random Variables H. Ghauch Mikael Skoglund H. S. Ghadikolaei Carlo Fischione A. H. Sayed 11 7 0 06 Jun 2018
Performance Metric Elicitation from Pairwise Classifier Comparisons G. Hiranandani Shant Boodaghians R. Mehta Oluwasanmi Koyejo 11 14 0 05 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 35 1,840 0 31 May 2018
Human-in-the-Loop Interpretability Prior Isaac Lage A. Ross Been Kim S. Gershman Finale Doshi-Velez 26 120 0 29 May 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 14 435 0 28 May 2018
Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World Yoshihide Sawada DRL 13 10 0 19 Apr 2018
Entanglement-guided architectures of machine learning by quantum tensor network Yuhan Liu Xiao Zhang M. Lewenstein Shi-Ju Ran 18 32 0 24 Mar 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 17 217 0 20 Mar 2018
Constant-Time Predictive Distributions for Gaussian Processes Geoff Pleiss J. Gardner Kilian Q. Weinberger A. Wilson 17 94 0 16 Mar 2018
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs Diviyan Kalainathan Olivier Goudet Isabelle M Guyon David Lopez-Paz Michèle Sebag CML 24 93 0 13 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick D. McDaniel OOD AAML 6 502 0 13 Mar 2018
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables Bojan Kolosnjaji Ambra Demontis Battista Biggio Davide Maiorca Giorgio Giacinto Claudia Eckert Fabio Roli AAML 14 315 0 12 Mar 2018
Explaining Black-box Android Malware Detection Marco Melis Davide Maiorca Battista Biggio Giorgio Giacinto Fabio Roli AAML FAtt 9 43 0 09 Mar 2018
The Challenge of Crafting Intelligible Intelligence Daniel S. Weld Gagan Bansal 15 241 0 09 Mar 2018
Teaching Categories to Human Learners with Visual Explanations Oisin Mac Aodha Shihan Su Yuxin Chen Pietro Perona Yisong Yue 8 70 0 20 Feb 2018
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 16 241 0 02 Feb 2018
Inverse Classification for Comparison-based Interpretability in Machine Learning Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 11 100 0 22 Dec 2017
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 31 1,789 0 30 Nov 2017
A Formal Framework to Characterize Interpretability of Procedures Amit Dhurandhar Vijay Iyengar Ronny Luss Karthikeyan Shanmugam 13 19 0 12 Jul 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 20 2,201 0 12 Jun 2017
Contextual Explanation Networks Maruan Al-Shedivat Kumar Avinava Dubey Eric P. Xing CML 22 82 0 29 May 2017