  Machine learning algorithms increasingly influence our decisions and interact
with us in all parts of our daily lives. Therefore, just as we consider the
safety of power plants, highways, and a variety of other engineered
socio-technical systems, we must also take into account the safety of systems
involving machine learning. Heretofore, the definition of safety has not been
formalized in a machine learning context. In this paper, we do so by defining
machine learning safety in terms of risk, epistemic uncertainty, and the harm
incurred by unwanted outcomes. We then use this definition to examine safety in
all sorts of applications in cyber-physical systems, decision sciences, and
data products. We find that the foundational principle of modern statistical
machine learning, empirical risk minimization, is not always a sufficient
objective. Finally, we discuss how four different categories of strategies for
achieving safety in engineering, including inherently safe design, safety
reserves, safe fail, and procedural safeguards can be mapped to a machine
learning context. We then discuss example techniques that can be adopted in
each category, such as considering interpretability and causality of predictive
models, objective functions beyond expected prediction accuracy, human
involvement for labeling difficult or rare examples, and user experience design
of software and open data.
