Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations

17 December 2021

Graham Neubig

Papers citing "Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations"

22 / 22 papers shown

Title
Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection Tanmay Chakraborty Christian Wirth Christin Seifert 26 0 0 01 Apr 2025
Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary Zhuoyan Li Ming Yin 25 3 0 02 Nov 2024
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem Qianli Wang Tatiana Anikina Nils Feldhus Simon Ostermann Sebastian Möller Vera Schmitt LRM 34 0 0 11 Sep 2024
Can Interpretability Layouts Influence Human Perception of Offensive Sentences? Thiago Freitas dos Santos Nardine Osman Marco Schorlemmer 19 0 0 01 Mar 2024
I-CEE: Tailoring Explanations of Image Classification Models to User Expertise Yao Rong Peizhu Qian Vaibhav Unhelkar Enkelejda Kasneci 30 0 0 19 Dec 2023
Representing visual classification as a linear combination of words Shobhit Agarwal Yevgeniy R. Semenov William Lotter 25 1 0 18 Nov 2023
Be Careful When Evaluating Explanations Regarding Ground Truth Hubert Baniecki Maciej Chrabaszcz Andreas Holzinger Bastian Pfeifer Anna Saranti P. Biecek FAtt AAML 40 3 0 08 Nov 2023
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations Nils Feldhus Qianli Wang Tatiana Anikina Sahil Chopra Cennet Oguz Sebastian Möller 26 9 0 09 Oct 2023
Explaining Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features Eliana Pastor Alkis Koudounas Giuseppe Attanasio Dirk Hovy Elena Baralis 11 4 0 14 Sep 2023
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies Jonas Teufel Luca Torresi Pascal Friederich FAtt 19 1 0 25 May 2023
Neighboring Words Affect Human Interpretation of Saliency Explanations Tim Dockhorn Yaoliang Yu Heike Adel Mahdi Zolnouri V. Nia FAtt MILM 28 3 0 04 May 2023
Assisting Human Decisions in Document Matching Joon Sik Kim Valerie Chen Danish Pruthi Nihar B. Shah Ameet Talwalkar 33 5 0 16 Feb 2023
Silent Vulnerable Dependency Alert Prediction with Vulnerability Key Aspect Explanation Jiamou Sun Zhenchang Xing Qinghua Lu Xiwei Xu Liming Zhu Thong Hoang Dehai Zhao 17 12 0 15 Feb 2023
Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations Yao Rong Tobias Leemann Thai-trang Nguyen Lisa Fiedler Peizhu Qian Vaibhav Unhelkar Tina Seidel Gjergji Kasneci Enkelejda Kasneci ELM 25 91 0 20 Oct 2022
Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods Nils Feldhus Leonhard Hennig Maximilian Dustin Nasert Christopher Ebert Robert Schwarzenberg Sebastian Möller FAtt 19 19 0 13 Oct 2022
Beware the Rationalization Trap! When Language Model Explainability Diverges from our Mental Models of Language R. Sevastjanova Mennatallah El-Assady LRM 27 9 0 14 Jul 2022
Mediators: Conversational Agents Explaining NLP Model Behavior Nils Feldhus A. Ravichandran Sebastian Möller 25 16 0 13 Jun 2022
Learning to Scaffold: Optimizing Model Explanations for Teaching Patrick Fernandes Marcos Vinícius Treviso Danish Pruthi André F. T. Martins Graham Neubig FAtt 17 22 0 22 Apr 2022
Robustness and Usefulness in AI Explanation Methods Erick Galinkin FAtt 20 1 0 07 Mar 2022
Human Interpretation of Saliency-based Explanation Over Text Hendrik Schuff Alon Jacovi Heike Adel Yoav Goldberg Ngoc Thang Vu MILM XAI FAtt 144 39 0 27 Jan 2022
Invariant Rationalization Shiyu Chang Yang Zhang Mo Yu Tommi Jaakkola 179 201 0 22 Mar 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017