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Explainable AI: introducing trust and comprehensibility to AI engineering.

, , and . Autom., 70 (9): 787-792 (2022)

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Explainable AI: introducing trust and comprehensibility to AI engineering., , and . Autom., 70 (9): 787-792 (2022)A Survey on the Explainability of Supervised Machine Learning., and . CoRR, (2020)Validation of XAI explanations for multivariate time series classification in the maritime domain., , , and . J. Comput. Sci., (2022)Batch-wise Regularization of Deep Neural Networks for Interpretability., , , and . MFI, page 216-222. IEEE, (2020)A Step Towards Global Counterfactual Explanations: Approximating the Feature Space Through Hierarchical Division and Graph Search., , , and . Adv. Artif. Intell. Mach. Learn., 1 (2): 90-110 (2021)Are you sure? Prediction revision in automated decision-making., , and . Expert Syst. J. Knowl. Eng., (2021)Evaluation of Interpretable Association Rule Mining Methods on Time-Series in the Maritime Domain., , and . ICPR Workshops (3), volume 12663 of Lecture Notes in Computer Science, page 204-218. Springer, (2020)Erklärbare Künstliche Intelligenz - Steigerung der Nachvollziehbarkeit überwachter maschineller Lernverfahren.. Karlsruhe Institute of Technology, Germany, (2022)A Survey on the Explainability of Supervised Machine Learning., and . J. Artif. Intell. Res., (2021)Supported Decision-Making by Explainable Predictions of Ship Trajectories., , and . SOCO, volume 1268 of Advances in Intelligent Systems and Computing, page 44-54. Springer, (2020)