Ensembling Uncertainty Measures to Improve Safety of Black-Box
  Classifiers
Tommaso Zoppi Andrea Ceccarelli Andrea Bondavalli
Machine Learning (ML) algorithms that perform classification may predict the
wrong class, experiencing misclassifications. It is well-known that
misclassifications may have cascading effects on the encompassing system,
possibly resulting in critical failures. This paper proposes SPROUT, a Safety
wraPper thROugh ensembles of UncertainTy measures, which suspects
misclassifications by computing uncertainty measures on the inputs and outputs
of a black-box classifier. If a misclassification is detected, SPROUT blocks
the propagation of the output of the classifier to the encompassing system. The
resulting impact on safety is that SPROUT transforms erratic outputs
(misclassifications) into data omission failures, which can be easily managed
at the system level. SPROUT has a broad range of applications as it fits binary
and multi-class classification, comprising image and tabular datasets. We
experimentally show that SPROUT always identifies a huge fraction of the
misclassifications of supervised classifiers, and it is able to detect all
misclassifications in specific cases. SPROUT implementation contains
pre-trained wrappers, it is publicly available and ready to be deployed with
minimal effort.