  Neural networks are used for many real world applications, but often they
have problems estimating their own confidence. This is particularly problematic
for computer vision applications aimed at making high stakes decisions with
humans and their lives. In this paper we make a meta-analysis of the
literature, showing that most if not all computer vision applications do not
use proper epistemic uncertainty quantification, which means that these models
ignore their own limitations. We describe the consequences of using models
without proper uncertainty quantification, and motivate the community to adopt
versions of the models they use that have proper calibrated epistemic
uncertainty, in order to enable out of distribution detection. We close the
paper with a summary of challenges on estimating uncertainty for computer
vision applications and recommendations.
