Do Concept Bottleneck Models Learn as Intended?

10 May 2021

Papers citing "Do Concept Bottleneck Models Learn as Intended?"

20 / 70 papers shown

Title
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis Ricards Marcinkevics Patricia Reis Wolfertstetter Ugne Klimiene Kieran Chin-Cheong Alyssia Paschke ... David Niederberger S. Wellmann Ece Ozkan C. Knorr Julia E. Vogt 14 25 0 28 Feb 2023
A Closer Look at the Intervention Procedure of Concept Bottleneck Models Sungbin Shin Yohan Jo Sungsoo Ahn Namhoon Lee 16 30 0 28 Feb 2023
Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation Jack Furby Daniel Cunnington Dave Braines Alun D. Preece 14 6 0 07 Feb 2023
Towards Robust Metrics for Concept Representation Evaluation M. Zarlenga Pietro Barbiero Z. Shams Dmitry Kazhdan Umang Bhatt Adrian Weller M. Jamnik 16 24 0 25 Jan 2023
Understanding and Enhancing Robustness of Concept-based Models Sanchit Sinha Mengdi Huai Jianhui Sun Aidong Zhang AAML 25 18 0 29 Nov 2022
Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions J. Lockhart Daniele Magazzeni Manuela Veloso 17 4 0 21 Nov 2022
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification Yue Yang Artemis Panagopoulou Shenghao Zhou Daniel Jin Chris Callison-Burch Mark Yatskar 40 211 0 21 Nov 2022
Towards learning to explain with concept bottleneck models: mitigating information leakage J. Lockhart Nicolas Marchesotti Daniele Magazzeni Manuela Veloso 17 6 0 07 Nov 2022
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction Sunnie S. Y. Kim E. A. Watkins Olga Russakovsky Ruth C. Fong A. Monroy-Hernández 38 107 0 02 Oct 2022
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations Jonathan Crabbé M. Schaar 56 46 0 22 Sep 2022
Leveraging Explanations in Interactive Machine Learning: An Overview Stefano Teso Öznur Alkan Wolfgang Stammer Elizabeth M. Daly XAI FAtt LRM 26 62 0 29 Jul 2022
Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability V. V. Ramaswamy Sunnie S. Y. Kim Ruth C. Fong Olga Russakovsky FAtt 22 27 0 20 Jul 2022
GlanceNets: Interpretabile, Leak-proof Concept-based Models Emanuele Marconato Andrea Passerini Stefano Teso 106 64 0 31 May 2022
Post-hoc Concept Bottleneck Models Mert Yuksekgonul Maggie Wang James Y. Zou 143 185 0 31 May 2022
Human-Centered Concept Explanations for Neural Networks Chih-Kuan Yeh Been Kim Pradeep Ravikumar FAtt 27 25 0 25 Feb 2022
Concept Bottleneck Model with Additional Unsupervised Concepts Yoshihide Sawada Keigo Nakamura SSL 11 66 0 03 Feb 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods Zohaib Salahuddin Henry C. Woodruff A. Chatterjee Philippe Lambin 15 301 0 01 Nov 2021
Promises and Pitfalls of Black-Box Concept Learning Models Anita Mahinpei Justin Clark Isaac Lage Finale Doshi-Velez Weiwei Pan 31 91 0 24 Jun 2021
Progressive Interpretation Synthesis: Interpreting Task Solving by Quantifying Previously Used and Unused Information Zhengqi He Taro Toyoizumi 15 1 0 08 Jan 2021