  Increasingly sophisticated mathematical modelling processes from Machine
Learning are being used to analyse complex data. However, the performance and
explainability of these models within practical critical systems requires a
rigorous and continuous verification of their safe utilisation. Working towards
addressing this challenge, this paper presents a principled novel safety
argument framework for critical systems that utilise deep neural networks. The
approach allows various forms of predictions, e.g., future reliability of
passing some demands, or confidence on a required reliability level. It is
supported by a Bayesian analysis using operational data and the recent
verification and validation techniques for deep learning. The prediction is
conservative -- it starts with partial prior knowledge obtained from lifecycle
activities and then determines the worst-case prediction. Open challenges are
also identified.
