Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

16 June 2020

Papers citing "Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey"

25 / 225 papers shown

Title
VitrAI -- Applying Explainable AI in the Real World Marc Hanussek Falko Kötter Maximilien Kintz Jens Drawehn 9 2 0 12 Feb 2021
A Taxonomy of Explainable Bayesian Networks I. Derks A. D. Waal BDL XAI 7 22 0 28 Jan 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
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 48 170 0 13 Jan 2021
Explainable Artificial Intelligence (XAI): An Engineering Perspective F. Hussain R. Hussain E. Hossain XAI 31 26 0 10 Jan 2021
Predicting Events in MOBA Games: Prediction, Attribution, and Evaluation Zelong Yang Yan Wang Piji Li Shaobin Lin Shuming Shi Shao-Lun Huang Wei Bi 15 12 0 17 Dec 2020
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization Sahil Suneja Yunhui Zheng Yufan Zhuang Jim Laredo Alessandro Morari AAML 32 32 0 25 Nov 2020
AI Governance for Businesses Johannes Schneider Rene Abraham Christian Meske Jan vom Brocke AI4TS 6 67 0 20 Nov 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 255 426 0 15 Oct 2020
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines A. Konstantinov Lev V. Utkin FedML AI4CE 10 138 0 14 Oct 2020
Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction Tom Weber S. Wermter 10 4 0 09 Oct 2020
Stuttering Speech Disfluency Prediction using Explainable Attribution Vectors of Facial Muscle Movements Arun Das J. Mock Henry Chacón Farzan Irani E. Golob Peyman Najafirad 6 9 0 02 Oct 2020
Graph representation forecasting of patient's medical conditions: towards a digital twin Pietro Barbiero Ramon Vinas Torné Pietro Lió AI4CE 18 12 0 17 Sep 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 41 62 0 11 Sep 2020
Learning from Few Samples: A Survey Nihar Bendre Hugo Terashima-Marín Peyman Najafirad VLM BDL 26 54 0 30 Jul 2020
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models Christoph Molnar Gunnar Konig J. Herbinger Timo Freiesleben Susanne Dandl Christian A. Scholbeck Giuseppe Casalicchio Moritz Grosse-Wentrup B. Bischl FAtt AI4CE 31 135 0 08 Jul 2020
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey S. Silva Peyman Najafirad AAML OOD 23 131 0 01 Jul 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 27 19 0 26 Jun 2020
Corpus-level and Concept-based Explanations for Interpretable Document Classification Tian Shi Xuchao Zhang Ping Wang Chandan K. Reddy FAtt 24 8 0 24 Apr 2020
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review F. Sado C. K. Loo W. S. Liew Matthias Kerzel S. Wermter 27 48 0 21 Apr 2020
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey Vanessa Buhrmester David Münch Michael Arens MLAU FaML XAI AAML 21 354 0 27 Nov 2019
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,690 0 28 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,842 0 08 Jul 2016