Understanding Disparities in Post Hoc Machine Learning Explanation

25 January 2024

Papers citing "Understanding Disparities in Post Hoc Machine Learning Explanation"

24 / 24 papers shown

Title
Diagnosing Model Performance Under Distribution Shift Tiffany Cai Hongseok Namkoong Steve Yadlowsky 101 36 0 03 Mar 2023
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation Julius Adebayo M. Muelly H. Abelson Been Kim 67 87 0 09 Dec 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 81 78 0 06 May 2022
Just Train Twice: Improving Group Robustness without Training Group Information Emmy Liu Behzad Haghgoo Annie S. Chen Aditi Raghunathan Pang Wei Koh Shiori Sagawa Percy Liang Chelsea Finn OOD 94 562 0 19 Jul 2021
What will it take to generate fairness-preserving explanations? Jessica Dai Sohini Upadhyay Stephen H. Bach Himabindu Lakkaraju FAtt FaML 48 14 0 24 Jun 2021
S-LIME: Stabilized-LIME for Model Explanation Zhengze Zhou Giles Hooker Fei Wang FAtt 143 91 0 15 Jun 2021
A Survey on the Explainability of Supervised Machine Learning Nadia Burkart Marco F. Huber FaML XAI 53 774 0 16 Nov 2020
Explainability for fair machine learning T. Begley Tobias Schwedes Christopher Frye Ilya Feige FaML FedML 84 47 0 14 Oct 2020
Causal Multi-Level Fairness Vishwali Mhasawade R. Chunara 47 27 0 14 Oct 2020
Ethical Machine Learning in Health Care Irene Y. Chen Emma Pierson Sherri Rose Shalmali Joshi Kadija Ferryman Marzyeh Ghassemi AILaw 74 387 0 22 Sep 2020
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability Dylan Slack Sophie Hilgard Sameer Singh Himabindu Lakkaraju FAtt 73 161 0 11 Aug 2020
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? Peter Hase Joey Tianyi Zhou FAtt 75 303 0 04 May 2020
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 61 198 0 22 Jul 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 91 322 0 27 May 2019
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 141 1,970 0 08 Oct 2018
Residual Unfairness in Fair Machine Learning from Prejudiced Data Nathan Kallus Angela Zhou FaML 176 136 0 07 Jun 2018
A Review of Challenges and Opportunities in Machine Learning for Health Marzyeh Ghassemi Tristan Naumann Peter F. Schulam Andrew L. Beam Irene Y. Chen Rajesh Ranganath 70 269 0 01 Jun 2018
Datasheets for Datasets Timnit Gebru Jamie Morgenstern Briana Vecchione Jennifer Wortman Vaughan Hanna M. Wallach Hal Daumé Kate Crawford 266 2,194 0 23 Mar 2018
Path-Specific Counterfactual Fairness Silvia Chiappa Thomas P. S. Gillam CML FaML 80 340 0 22 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 115 2,360 0 01 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,939 0 22 May 2017
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 321 20,070 0 07 Oct 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,027 0 16 Feb 2016
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 72 743 0 05 Nov 2015