Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

20 March 2018

Papers citing "Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges"

21 / 21 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 24 0 0 09 May 2025
Policy-to-Language: Train LLMs to Explain Decisions with Flow-Matching Generated Rewards Xinyi Yang Liang Zeng Heng Dong C. Yu X. Wu H. Yang Yu Wang Milind Tambe Tonghan Wang 68 2 0 18 Feb 2025
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle Luca Deck Astrid Schomacker Timo Speith Jakob Schöffer Lena Kästner Niklas Kühl 33 4 0 29 Apr 2024
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 29 1 0 17 Feb 2023
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations Zixi Chen Varshini Subhash Marton Havasi Weiwei Pan Finale Doshi-Velez XAI FAtt 27 18 0 10 Nov 2022
A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices Liang Huang Senjie Liang Feiyang Ye Nan Gao 48 3 0 16 May 2022
The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences A. McGovern I. Ebert‐Uphoff D. Gagne A. Bostrom 17 64 0 15 Dec 2021
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers Zunlei Feng Jiacong Hu Sai Wu Xiaotian Yu Jie Song Mingli Song 21 13 0 09 Dec 2021
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities Waddah Saeed C. Omlin XAI 34 414 0 11 Nov 2021
What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data Yang Hu Adriane P. Chapman Guihua Wen Dame Wendy Hall 34 24 0 11 Jun 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 16 137 0 17 May 2021
Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity Mathias Parisot Balázs Pejó Dayana Spagnuelo MIACV 19 33 0 27 Apr 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 34 126 0 24 Jan 2021
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 29 370 0 30 Apr 2020
From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability I. Laña J. S. Medina E. Vlahogianni Javier Del Ser 19 51 0 06 Feb 2020
Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support Christian Meske Enrico Bunde 17 7 0 04 Feb 2020
CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis Yao Xie Melody Chen David Kao Ge Gao Xiang Ánthony' Chen 23 125 0 15 Jan 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 35 701 0 08 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot S. Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 11 6,106 0 22 Oct 2019
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems Richard J. Tomsett Dave Braines Daniel Harborne Alun D. Preece Supriyo Chakraborty FaML 21 164 0 20 Jun 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,233 0 24 Jun 2017