  Machine Learning (ML) is now used in a range of systems with results that are
reported to exceed, under certain conditions, human performance. Many of these
systems, in domains such as healthcare , automotive and manufacturing, exhibit
high degrees of autonomy and are safety critical. Establishing justified
confidence in ML forms a core part of the safety case for these systems. In
this document we introduce a methodology for the Assurance of Machine Learning
for use in Autonomous Systems (AMLAS). AMLAS comprises a set of safety case
patterns and a process for (1) systematically integrating safety assurance into
the development of ML components and (2) for generating the evidence base for
explicitly justifying the acceptable safety of these components when integrated
into autonomous system applications.
