Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with mali...

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Veröffentlicht in:	arXiv.org 2021-04
Hauptverfasser:	Houben, Sebastian, Abrecht, Stephanie, Maram Akila, Bär, Andreas, Brockherde, Felix, Feifel, Patrick, Fingscheidt, Tim, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Hammam, Ahmed, Haselhoff, Anselm, Hauser, Felix, Heinzemann, Christian, Hoffmann, Marco, Kapoor, Nikhil, Kappel, Falk, Klingner, Marvin, Kronenberger, Jan, Küppers, Fabian, Löhdefink, Jonas, Mlynarski, Michael, Mock, Michael, Mualla, Firas, Pavlitskaya, Svetlana, Poretschkin, Maximilian, Pohl, Alexander, Ravi-Kumar, Varun, Rosenzweig, Julia, Rottmann, Matthias, Rüping, Stefan, Sämann, Timo, Schneider, Jan David, Schulz, Elena, Schwalbe, Gesina, Sicking, Joachim, Srivastava, Toshika, Serin Varghese, Weber, Michael, Wirkert, Sebastian, Wirtz, Tim, Woehrle, Matthias
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Computer Science - Computers and Society Computer Science - Learning Cyber-physical systems Machine learning Safety critical
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