  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
malicious inputs. Cyber-physical systems employing DNNs are therefore likely to
suffer from safety concerns. In recent years, a zoo of state-of-the-art
techniques aiming to address these safety concerns has emerged. This work
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 paper addresses both machine
learning experts and safety engineers: 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 ML methods. We moreover hope that our contribution fuels discussions on
desiderata for ML systems and strategies on how to propel existing approaches
accordingly.
