  The utilisation of Deep Learning (DL) is advancing into increasingly more
sophisticated applications. While it shows great potential to provide
transformational capabilities, DL also raises new challenges regarding its
reliability in critical functions. In this paper, we present a model-agnostic
reliability assessment method for DL classifiers, based on evidence from
robustness evaluation and the operational profile (OP) of a given application.
We partition the input space into small cells and then "assemble" their
robustness (to the ground truth) according to the OP, where estimators on the
cells' robustness and OPs are provided. Reliability estimates in terms of the
probability of misclassification per input (pmi) can be derived together with
confidence levels. A prototype tool is demonstrated with simplified case
studies. Model assumptions and extension to real-world applications are also
discussed. While our model easily uncovers the inherent difficulties of
assessing the DL dependability (e.g. lack of data with ground truth and
scalability issues), we provide preliminary/compromised solutions to advance in
this research direction.
