Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study

International Conference on Intelligent User Interfaces (IUI), 2020

3 February 2020

Papers citing "Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study"

40 / 90 papers shown

Title
Assessing Out-of-Domain Language Model Performance from Few ExamplesConference of the European Chapter of the Association for Computational Linguistics (EACL), 2022 Prasann Singhal Jarad Forristal Xi Ye Greg Durrett LRM 182 6 0 13 Oct 2022
Responsibility: An Example-based Explainable AI approach via Training Process Inspection Faraz Khadivpour Arghasree Banerjee Matthew J. Guzdial XAI 153 2 0 07 Sep 2022
Monitoring Shortcut Learning using Mutual Information Mohammed Adnan Yani Andrew Ioannou Chuan-Yung Tsai A. Galloway H. R. Tizhoosh Graham W. Taylor 106 7 0 27 Jun 2022
Comparison of attention models and post-hoc explanation methods for embryo stage identification: a case study T. Gomez Thomas Fréour Harold Mouchère 148 3 0 13 May 2022
Towards a multi-stakeholder value-based assessment framework for algorithmic systemsConference on Fairness, Accountability and Transparency (FAccT), 2022 Mireia Yurrita Dave Murray-Rust Agathe Balayn A. Bozzon MLAU 200 36 0 09 May 2022
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And DatasetInternational Conference on Learning Representations (ICLR), 2022 Leon Sixt M. Schuessler Oana-Iuliana Popescu Philipp Weiß Tim Landgraf FAtt 189 18 0 25 Apr 2022
Perception Visualization: Seeing Through the Eyes of a DNNBritish Machine Vision Conference (BMVC), 2022 Loris Giulivi Mark J. Carman Giacomo Boracchi 126 6 0 21 Apr 2022
Explainable Predictive Process Monitoring: A User Evaluation Williams Rizzi M. Comuzzi Chiara Di Francescomarino Chiara Ghidini Suhwan Lee F. Maggi Alexander Nolte FaML XAI 199 14 0 15 Feb 2022
Machine Explanations and Human Understanding Chacha Chen Shi Feng Amit Sharma Chenhao Tan 298 31 0 08 Feb 2022
Algorithmic nudge to make better choices: Evaluating effectiveness of XAI frameworks to reveal biases in algorithmic decision making to users Prerna Juneja Tanushree Mitra CML 80 0 0 05 Feb 2022
Metrics for saliency map evaluation of deep learning explanation methodsInternational Conferences on Pattern Recognition and Artificial Intelligence (ICCPRAI), 2022 T. Gomez Thomas Fréour Harold Mouchère XAI FAtt 271 51 0 31 Jan 2022
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 165 1 0 30 Jan 2022
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies Vivian Lai Chacha Chen Q. V. Liao Alison Smith-Renner Chenhao Tan 249 208 0 21 Dec 2021
Explainability Pitfalls: Beyond Dark Patterns in Explainable AI Upol Ehsan Mark O. Riedl XAI SILM 151 77 0 26 Sep 2021
Enhancing Model Assessment in Vision-based Interactive Machine Teaching through Real-time Saliency Map VisualizationACM Symposium on User Interface Software and Technology (UIST), 2021 Zhongyi Zhou Koji Yatani FAtt 60 5 0 26 Aug 2021
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability Roman Levin Manli Shu Eitan Borgnia Furong Huang Micah Goldblum Tom Goldstein FAtt AAML 87 12 0 03 Aug 2021
Temporal Dependencies in Feature Importance for Time Series PredictionsInternational Conference on Learning Representations (ICLR), 2021 Kin Kwan Leung Clayton Rooke Jonathan Smith S. Zuberi Anthony L. Caterini OOD AI4TS 177 35 0 29 Jul 2021
The Who in XAI: How AI Background Shapes Perceptions of AI ExplanationsInternational Conference on Human Factors in Computing Systems (CHI), 2021 Upol Ehsan Samir Passi Q. V. Liao Larry Chan I-Hsiang Lee Michael J. Muller Mark O. Riedl 201 108 0 28 Jul 2021
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and SolutionsIEEE Access (IEEE Access), 2021 Eike Petersen Yannik Potdevin Esfandiar Mohammadi Stephan Zidowitz Sabrina Breyer ... Sandra Henn Ludwig Pechmann M. Leucker P. Rostalski Christian Herzog FaML AILaw OOD 217 34 0 20 Jul 2021
Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI) J. H. Hsiao H. Ngai Luyu Qiu Yi Yang Caleb Chen Cao XAI 126 32 0 20 Jul 2021
Challenges for machine learning in clinical translation of big data imaging studies Nicola K. Dinsdale Emma Bluemke V. Sundaresan M. Jenkinson Stephen Smith Ana I. L. Namburete AI4CE 183 61 0 07 Jul 2021
Evaluation of Saliency-based Explainability Method Sam Zabdiel Sunder Samuel V. Kamakshi Namrata Lodhi N. C. Krishnan FAtt XAI 147 16 0 24 Jun 2021
Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations Abubakar Abid Mert Yuksekgonul James Zou CML 176 72 0 24 Jun 2021
The effectiveness of feature attribution methods and its correlation with automatic evaluation scoresNeural Information Processing Systems (NeurIPS), 2021 Giang Nguyen Daeyoung Kim Anh Totti Nguyen FAtt 339 101 0 31 May 2021
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments M. Schuessler Philipp Weiß Leon Sixt 127 3 0 06 May 2021
Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer LearningApplied Soft Computing (Appl Soft Comput), 2021 Navid Ghassemi A. Shoeibi Marjane Khodatars Jónathan Heras Alireza Rahimi A. Zare R. B. Pachori Juan M Gorriz MedIm 162 67 0 24 Apr 2021
LEx: A Framework for Operationalising Layers of Machine Learning Explanations Ronal Singh Upol Ehsan M. Cheong Mark O. Riedl Tim Miller 96 5 0 15 Apr 2021
Question-Driven Design Process for Explainable AI User Experiences Q. V. Liao Milena Pribić Jaesik Han Sarah Miller Daby M. Sow 254 63 0 08 Apr 2021
Expanding Explainability: Towards Social Transparency in AI systemsInternational Conference on Human Factors in Computing Systems (CHI), 2021 Upol Ehsan Q. V. Liao Michael J. Muller Mark O. Riedl Justin D. Weisz 224 471 0 12 Jan 2021
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial LearningFrontiers in Artificial Intelligence (FAI), 2020 Silvan Mertes Tobias Huber Katharina Weitz Alexander Heimerl Elisabeth André GAN AAML MedIm 284 100 0 22 Dec 2020
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learningInternational Conference on Human Factors in Computing Systems (CHI), 2020 Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 275 19 0 10 Dec 2020
Representaciones del aprendizaje reutilizando los gradientes de la retropropagacion Roberto Reyes-Ochoa Servando Lopez-Aguayo FAtt 87 0 0 06 Dec 2020
Debugging Tests for Model Explanations Julius Adebayo M. Muelly Ilaria Liccardi Been Kim FAtt 254 197 0 10 Nov 2020
Towards falsifiable interpretability research Matthew L. Leavitt Ari S. Morcos AAML AI4CE 156 73 0 22 Oct 2020
Measure Utility, Gain Trust: Practical Advice for XAI Researcher B. Pierson M. Glenski William I. N. Sealy Dustin L. Arendt 135 29 0 27 Sep 2020
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps Tobias Huber Katharina Weitz Elisabeth André Ofra Amir FAtt 280 70 0 18 May 2020
Explainable Goal-Driven Agents and Robots -- A Comprehensive ReviewACM Computing Surveys (ACM CSUR), 2020 F. Sado C. K. Loo W. S. Liew Matthias Kerzel S. Wermter 307 68 0 21 Apr 2020
How to Support Users in Understanding Intelligent Systems? Structuring the DiscussionInternational Conference on Intelligent User Interfaces (IUI), 2020 Malin Eiband Daniel Buschek H. Hussmann 221 30 0 22 Jan 2020
Secure and Robust Machine Learning for Healthcare: A SurveyIEEE Reviews in Biomedical Engineering (RBME), 2020 A. Qayyum Junaid Qadir Muhammad Bilal Ala I. Al-Fuqaha AAML OOD 208 434 0 21 Jan 2020
When Explanations Lie: Why Many Modified BP Attributions FailInternational Conference on Machine Learning (ICML), 2019 Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 476 141 0 20 Dec 2019