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Machine Learning (ML) is now used in a range of systems with results that arereported to exceed, under certain conditions, human performance. Many of thesesystems, in domains such as healthcare , automotive and manufacturing, exhibithigh degrees of autonomy and are safety critical. Establishing justifiedconfidence in ML forms a core part of the safety case for these systems. Inthis document we introduce a methodology for the Assurance of Machine Learningfor use in Autonomous Systems (AMLAS). AMLAS comprises a set of safety casepatterns and a process for (1) systematically integrating safety assurance intothe development of ML components and (2) for generating the evidence base forexplicitly justifying the acceptable safety of these components when integratedinto autonomous system applications.

Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)