Adrian Schwaiger, research engineer at the Fraunhofer Institute for Cognitive Systems IKS, will be presenting his new paper »Benchmarking Uncertainty Estimation Methods for Deep Learning With Safety-Related Metrics« at the SafeAI workshop. The workshop belongs to the AAAI-20 Conference in New York City. In this paper, Adrian Schwaiger and his colleagues Maximilian Henne, Karsten Roscher and Gereon Weiss compare current models of measuring uncertainty in deep neural networks in order to optimally combine the safety and performance of cognitive systems. Deep neural networks generally perform very well in giving accurate predictions, but they often fail in recognizing when these predictions may be wrong. This absence of awareness regarding the reliability of given outputs is a big obstacle in deploying such models in safety-critical applications for example in autonomous vehicles or in medical technology.