How Useful Are the Machine-Generated Interpretations to General Users? A
Human Evaluation on Guessing the Incorrectly Predicted Labels

How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels

26 August 2020

Hua Shen

Ting-Hao 'Kenneth' Huang

Papers citing "How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels"

11 / 11 papers shown

Title
What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions Somayeh Molaei Lionel P. Robert Nikola Banovic 26 0 0 09 May 2025
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers Lokesh Badisa Sumohana S. Channappayya 45 0 0 17 Jun 2024
Graphical Perception of Saliency-based Model Explanations Yayan Zhao Mingwei Li Matthew Berger XAI FAtt 49 2 0 11 Jun 2024
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods Robin Hesse Simone Schaub-Meyer Stefan Roth AAML 37 33 0 11 Aug 2023
CRAFT: Concept Recursive Activation FacTorization for Explainability Thomas Fel Agustin Picard Louis Bethune Thibaut Boissin David Vigouroux Julien Colin Rémi Cadène Thomas Serre 19 103 0 17 Nov 2022
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy Giang Nguyen Mohammad Reza Taesiri Anh Totti Nguyen 30 42 0 26 Jul 2022
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset Leon Sixt M. Schuessler Oana-Iuliana Popescu Philipp Weiß Tim Landgraf FAtt 29 14 0 25 Apr 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
Explaining the Road Not Taken Hua Shen Ting-Hao 'Kenneth' Huang FAtt XAI 27 9 0 27 Mar 2021
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 39 7 0 23 Oct 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,690 0 28 Feb 2017