Burkart, NadiaNadiaBurkartHuber, MarcoMarcoHuberAnneken, MathiasMathiasAnneken2022-03-142022-03-142021https://publica.fraunhofer.de/handle/publica/40904010.1007/978-3-030-57802-2_5Machine Learning and Deep Learning models make accurate predictions based on a specifically trained task. For instance, models that classify ship vessel types based on their trajectory and other features. This can support human experts while they try to obtain information on the ships, e.g., to control illegal fishing. Besides the support in predicting a certain ship type, there is a need to explain the decision-making behind the classification. For example, which features contributed the most to the classification of the ship type. This paper introduces existing explanation approaches to the task of ship classification. The underlying model is based on a Residual Neural Network. The model was trained on an AIS data set. Further, we illustrate the explainability approaches by means of an explanatory case study and conduct a first experiment with a human expert.enmachine learningblack boxexplainabilityinterpretabilitytrustKünstliche Intelligenzneuronales NetzEntscheidungsfindungExplainable Artificial Intelligence (XAI)maschinelles Lernen004670conference paper