Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge

27 September 2022

Cara L. Widmer

Md Kamruzzaman Sarker

Papers citing "Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge"

23 / 23 papers shown

Title
Aligning Generalisation Between Humans and Machines Filip Ilievski Barbara Hammer F. V. Harmelen Benjamin Paassen S. Saralajew ... Vered Shwartz Gabriella Skitalinskaya Clemens Stachl Gido M. van de Ven T. Villmann 282 1 0 23 Nov 2024
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 127 6,293 0 22 Oct 2019
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models Daniel Omeiza Skyler Speakman C. Cintas Komminist Weldemariam FAtt 60 218 0 03 Aug 2019
Bayesian Item Response Modeling in R with brms and Stan Paul-Christian Bürkner 44 390 0 23 May 2019
Understanding Neural Networks via Feature Visualization: A survey Anh Nguyen J. Yosinski Jeff Clune FAtt 76 161 0 18 Apr 2019
Counterfactual Visual Explanations Yash Goyal Ziyan Wu Jan Ernst Dhruv Batra Devi Parikh Stefan Lee CML 80 512 0 16 Apr 2019
Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract] Yuxia Geng Jiaoyan Chen Ernesto Jiménez-Ruiz Huajun Chen 20 12 0 20 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 201 1,444 0 14 Jan 2019
Metrics for Explainable AI: Challenges and Prospects R. Hoffman Shane T. Mueller Gary Klein Jordan Litman XAI 86 728 0 11 Dec 2018
Efficient Concept Induction for Description Logics Md Kamruzzaman Sarker Pascal Hitzler 47 38 0 08 Dec 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 141 1,969 0 08 Oct 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 84 528 0 21 Jun 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 93 700 0 21 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 104 243 0 02 Feb 2018
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 219 1,849 0 30 Nov 2017
Distilling a Neural Network Into a Soft Decision Tree Nicholas Frosst Geoffrey E. Hinton 415 638 0 27 Nov 2017
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 104 688 0 02 Nov 2017
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Zou FAtt AAML 133 870 0 29 Oct 2017
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps Md Kamruzzaman Sarker Ning Xie Derek Doran M. Raymer Pascal Hitzler 3DV 43 64 0 11 Oct 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,018 0 22 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 216 2,905 0 14 Mar 2017
Semantic Understanding of Scenes through the ADE20K Dataset Bolei Zhou Hang Zhao Xavier Puig Tete Xiao Sanja Fidler Adela Barriuso Antonio Torralba SSeg 404 1,890 0 18 Aug 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,033 0 16 Feb 2016