Machine Learning Explainability for External Stakeholders

10 July 2020

Papers citing "Machine Learning Explainability for External Stakeholders"

30 / 30 papers shown

Title
Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antorán Umang Bhatt T. Adel Adrian Weller José Miguel Hernández-Lobato UQCV BDL 94 116 0 11 Jun 2020
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? Peter Hase Joey Tianyi Zhou FAtt 79 304 0 04 May 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 97 226 0 01 May 2020
Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems Zana Buçinca Phoebe Lin Krzysztof Z. Gajos Elena L. Glassman ELM 84 288 0 22 Jan 2020
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making Yunfeng Zhang Q. V. Liao Rachel K. E. Bellamy 94 682 0 07 Jan 2020
ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles Inioluwa Deborah Raji Jingyi Yang 90 38 0 12 Dec 2019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 81 822 0 06 Nov 2019
Addressing Failure Prediction by Learning Model Confidence Charles Corbière Nicolas Thome Avner Bar-Hen Matthieu Cord P. Pérez 122 291 0 01 Oct 2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind ... Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis L. Wei Yunfeng Zhang XAI 74 393 0 06 Sep 2019
Why Authors Don't Visualize Uncertainty Jessica Hullman 61 126 0 05 Aug 2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Yaniv Ovadia Emily Fertig Jie Jessie Ren Zachary Nado D. Sculley Sebastian Nowozin Joshua V. Dillon Balaji Lakshminarayanan Jasper Snoek UQCV 192 1,706 0 06 Jun 2019
Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design Neil D. Lawrence AI4CE PINN 55 6 0 26 Mar 2019
On Network Science and Mutual Information for Explaining Deep Neural Networks Brian Davis Umang Bhatt Kartikeya Bhardwaj R. Marculescu J. M. F. Moura FedML SSL FAtt 51 10 0 20 Jan 2019
Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh Joon Sik Kim Ian En-Hsu Yen Pradeep Ravikumar TDI 94 254 0 23 Nov 2018
Interpreting Black Box Predictions using Fisher Kernels Rajiv Khanna Been Kim Joydeep Ghosh Oluwasanmi Koyejo FAtt 85 104 0 23 Oct 2018
Model Cards for Model Reporting Margaret Mitchell Simone Wu Andrew Zaldivar Parker Barnes Lucy Vasserman Ben Hutchinson Elena Spitzer Inioluwa Deborah Raji Timnit Gebru 146 1,910 0 05 Oct 2018
Actionable Recourse in Linear Classification Berk Ustun Alexander Spangher Yang Liu FaML 129 551 0 18 Sep 2018
The Social Cost of Strategic Classification S. Milli John Miller Anca Dragan Moritz Hardt 51 184 0 25 Aug 2018
Accurate Uncertainties for Deep Learning Using Calibrated Regression Volodymyr Kuleshov Nathan Fenner Stefano Ermon BDL UQCV 209 636 0 01 Jul 2018
Datasheets for Datasets Timnit Gebru Jamie Morgenstern Briana Vecchione Jennifer Wortman Vaughan Hanna M. Wallach Hal Daumé Kate Crawford 296 2,201 0 23 Mar 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 110 701 0 21 Feb 2018
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 129 592 0 21 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 140 2,371 0 01 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 261 4,287 0 22 Jun 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,877 0 14 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,135 0 22 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 229 2,910 0 14 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 422 3,824 0 28 Feb 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,716 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,092 0 16 Feb 2016