  Both industry and academia have made considerable progress in developing
trustworthy and responsible machine learning (ML) systems. While critical
concepts like fairness and explainability are often addressed, the safety of
systems is typically not sufficiently taken into account. By viewing
data-driven decision systems as socio-technical systems, we draw on the
uncertainty in ML literature to show how fairML systems can also be safeML
systems. We posit that a fair model needs to be an uncertainty-aware model,
e.g. by drawing on distributional regression. For fair decisions, we argue that
a safe fail option should be used for individuals with uncertain
categorization. We introduce semi-structured deep distributional regression as
a modeling framework which addresses multiple concerns brought against standard
ML models and show its use in a real-world example of algorithmic profiling of
job seekers.
