  We investigate the issues of achieving sufficient rigor in the arguments for
the safety of machine learning functions. By considering the known weaknesses
of DNN-based 2D bounding box detection algorithms, we sharpen the metric of
imprecise pedestrian localization by associating it with the safety goal. The
sharpening leads to introducing a conservative post-processor after the
standard non-max-suppression as a counter-measure. We then propose a
semi-formal assurance case for arguing the effectiveness of the post-processor,
which is further translated into formal proof obligations for demonstrating the
soundness of the arguments. Applying theorem proving not only discovers the
need to introduce missing claims and mathematical concepts but also reveals the
limitation of Dempster-Shafer's rules used in semi-formal argumentation.
