  Machine learning (ML) has recently created many new success stories. Hence,
there is a strong motivation to use ML technology in software-intensive
systems, including safety-critical systems. This raises the issue of safety
verification of MLbased systems, which is currently thought to be infeasible
or, at least, very hard. We think that it requires taking into account specific
properties of ML technology such as: (i) Most ML approaches are inductive,
which is both their power and their source of error. (ii) Neural networks (NN)
resulting from deep learning are at the current state of the art not
transparent. Consequently, there will always be errors remaining and, at least
for deep NNs (DNNs), verification of their internal structure is extremely
hard. In general, safety engineering cannot provide full guarantees that no
harm will ever occur. That is why probabilities are used, e.g., for specifying
a risk or a Tolerable Hazard Rate (THR). In this vision paper, we propose
verification based on probabilities of errors both estimated by controlled
experiments and output by the inductively learned classifier itself.
Generalization error bounds may propagate to the probabilities of a hazard,
which must not exceed a THR. As a result, the quantitatively determined bound
on the probability of a classification error of an ML component in a
safety-critical system contributes in a well-defined way to the latter's
overall safety verification.
