Meaningfully Debugging Model Mistakes using Conceptual Counterfactual
Explanations

Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations

24 June 2021

Mert Yuksekgonul

Papers citing "Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations"

12 / 12 papers shown

Title
Concept-Based Unsupervised Domain Adaptation Xinyue Xu Y. Hu Hui Tang Yi Qin Lu Mi Hao Wang Xiaomeng Li 50 0 0 08 May 2025
GIFT: A Framework for Global Interpretable Faithful Textual Explanations of Vision Classifiers Éloi Zablocki Valentin Gerard Amaia Cardiel Eric Gaussier Matthieu Cord Eduardo Valle 76 0 0 23 Nov 2024
Black-Box Access is Insufficient for Rigorous AI Audits Stephen Casper Carson Ezell Charlotte Siegmann Noam Kolt Taylor Lynn Curtis ... Michael Gerovitch David Bau Max Tegmark David M. Krueger Dylan Hadfield-Menell AAML 17 76 0 25 Jan 2024
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? Sonia Laguna Ricards Marcinkevics Moritz Vandenhirtz Julia E. Vogt 19 17 0 24 Jan 2024
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation Wenhao Ding Laixi Shi Yuejie Chi Ding Zhao OOD 27 18 0 15 Jul 2023
Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal M. Hashemi Ali Darejeh Francisco Cruz 37 3 0 07 Feb 2023
Understanding the Complexity and Its Impact on Testing in ML-Enabled Systems Junming Cao Bihuan Chen Longjie Hu Jie Ying Gao Kaifeng Huang Xin Peng 8 3 0 10 Jan 2023
ModelDiff: A Framework for Comparing Learning Algorithms Harshay Shah Sung Min Park Andrew Ilyas A. Madry SyDa 46 26 0 22 Nov 2022
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning Olivia Wiles Isabela Albuquerque Sven Gowal VLM 30 46 0 18 Aug 2022
Distilling Model Failures as Directions in Latent Space Saachi Jain Hannah Lawrence Ankur Moitra A. Madry 16 89 0 29 Jun 2022
Post-hoc Concept Bottleneck Models Mert Yuksekgonul Maggie Wang James Y. Zou 138 185 0 31 May 2022
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset Matthew Groh Caleb Harris L. Soenksen Felix Lau Rachel Han Aerin Kim A. Koochek Omar Badri 104 181 0 20 Apr 2021