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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

, , , , , , , , , , , and . (2019)cite arxiv:1910.10045Comment: 67 pages, 13 figures, accepted for its publication in Information Fusion.

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Validation Techniques for Sensor Data in Mobile Health Applications., , , , and . J. Sensors, (2016)A Framework for Context-Aware Applications for Smart Spaces.. SAINT, page 218-221. IEEE Computer Society, (2011)Deep unsupervised state representation learning with robotic priors: a robustness analysis., , , , and . IJCNN, page 1-8. IEEE, (2019)A semantic security framework and context-aware role-based access control ontology for smart spaces., , , and . SBD@SIGMOD, page 8. ACM, (2016)Accessible Cultural Heritage through Explainable Artificial Intelligence., and . UMAP (Adjunct Publication), page 317-324. ACM, (2020)Unsupervised state representation learning with robotic priors: a robustness benchmark., , , , and . CoRR, (2017)Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities., , , , , , , , , and 13 other author(s). Frontiers Big Data, (2020)Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification., , , , and . CoRR, (2022)Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors., , , , and . UCAmI, volume 8276 of Lecture Notes in Computer Science, page 254-261. Springer, (2013)State Representation Learning for Control: An Overview., , , and . CoRR, (2018)