  Machine learning algorithms are increasingly influencing our decisions and
interacting with us in all parts of our daily lives. Therefore, just like for
power plants, highways, and myriad other engineered sociotechnical systems, we
must consider the safety of systems involving machine learning. In this paper,
we first discuss the definition of safety in terms of risk, epistemic
uncertainty, and the harm incurred by unwanted outcomes. Then we examine
dimensions, such as the choice of cost function and the appropriateness of
minimizing the empirical average training cost, along which certain real-world
applications may not be completely amenable to the foundational principle of
modern statistical machine learning: empirical risk minimization. In
particular, we note an emerging dichotomy of applications: ones in which safety
is important and risk minimization is not the complete story (we name these
Type A applications), and ones in which safety is not so critical and risk
minimization is sufficient (we name these Type B applications). Finally, we
discuss how four different strategies for achieving safety in engineering
(inherently safe design, safety reserves, safe fail, and procedural safeguards)
can be mapped to the machine learning context through interpretability and
causality of predictive models, objectives beyond expected prediction accuracy,
human involvement for labeling difficult or rare examples, and user experience
design of software.
