  The increasing use of Machine Learning (ML) components embedded in autonomous
systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the
pressing need to assure their functional safety. As for traditional functional
safety, the emerging consensus within both, industry and academia, is to use
assurance cases for this purpose. Typically assurance cases support claims of
reliability in support of safety, and can be viewed as a structured way of
organising arguments and evidence generated from safety analysis and
reliability modelling activities. While such assurance activities are
traditionally guided by consensus-based standards developed from vast
engineering experience, LESs pose new challenges in safety-critical application
due to the characteristics and design of ML models. In this article, we first
present an overall assurance framework for LESs with an emphasis on
quantitative aspects, e.g., breaking down system-level safety targets to
component-level requirements and supporting claims stated in reliability
metrics. We then introduce a novel model-agnostic Reliability Assessment Model
(RAM) for ML classifiers that utilises the operational profile and robustness
verification evidence. We discuss the model assumptions and the inherent
challenges of assessing ML reliability uncovered by our RAM and propose
solutions to practical use. Probabilistic safety argument templates at the
lower ML component-level are also developed based on the RAM. Finally, to
evaluate and demonstrate our methods, we not only conduct experiments on
synthetic/benchmark datasets but also scope our methods with case studies on
simulated Autonomous Underwater Vehicles and physical Unmanned Ground Vehicles.
