@incollection{HoubenAbrechtAkilaetal.2022,
  author    = {Houben, Sebastian and Abrecht, Stephanie and Akila, Maram and B{\"a}r, Andreas and Brockherde, Felix and Feifel, Patrick and Fingscheidt, Tim and Gannamaneni, Sujan Sai and Ghobadi, Seyed Eghbal and Hammam, Ahmed and Haselhoff, Anselm and Hauser, Felix and Heinzemann, Christian and Hoffmann, Marco and Kapoor, Nikhil and Kappel, Falk and Klingner, Marvin and Kronenberger, Jan and K{\"u}ppers, Fabian and L{\"o}hdefink, Jonas and Mlynarski, Michael and Mock, Michael and Mualla, Firas and Pavlitskaya, Svetlana and Poretschkin, Maximilian and Pohl, Alexander and Ravi-Kumar, Varun and Rosenzweig, Julia and Rottmann, Matthias and R{\"u}ping, Stefan and S{\"a}mann, Timo and Schneider, Jan David and Schulz, Elena and Schwalbe, Gesina and Sicking, Joachim and Srivastava, Toshika and Varghese, Serin and Weber, Michael and Wirkert, Sebastian and Wirtz, Tim and Woehrle, Matthias},
  title     = {Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety},
  booktitle = {Fingscheidt, Gottschalk et al. (Hg.): Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety},
  isbn      = {978-3-031-01232-7},
  doi       = {10.1007/978-3-031-01233-4_1},
  institution = {Fachbereich Informatik},
  publisher      = {Hochschule Bonn-Rhein-Sieg},
  pages     = {3 -- 78},
  year      = {2022},
  abstract  = {Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly.},
  language  = {en}
}