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  4. Supported Decision-Making by Explainable Predictions of Ship Trajectories
 
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2021
Conference Paper
Title

Supported Decision-Making by Explainable Predictions of Ship Trajectories

Abstract
Machine 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.
Author(s)
Burkart, Nadia  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Huber, Marco  
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Anneken, Mathias  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Mainwork
15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020  
Conference
International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO) 2020  
DOI
10.1007/978-3-030-57802-2_5
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Keyword(s)
  • machine learning

  • black box

  • explainability

  • interpretability

  • trust

  • Künstliche Intelligenz

  • neuronales Netz

  • Entscheidungsfindung

  • Explainable Artificial Intelligence (XAI)

  • maschinelles Lernen

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