  Out-of-distribution (OoD) detection techniques are instrumental for
safety-related neural networks. We are arguing, however, that current
performance-oriented OoD detection techniques geared towards matching metrics
such as expected calibration error, are not sufficient for establishing safety
claims. What is missing is a rigorous design approach for developing,
verifying, and validating OoD detectors. These design principles need to be
aligned with the intended functionality and the operational domain. Here, we
formulate some of the key technical challenges, together with a possible way
forward, for developing a rigorous and safety-related design methodology for
OoD detectors.
