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.

翻译：机器学习（ML）分类算法可能预测错误类别，导致误分类。众所周知，误分类可能对系统整体产生级联效应，甚至引发严重故障。本文提出SPROUT——一种通过集成不确定性度量的安全封装器，它通过计算黑盒分类器输入和输出的不确定性度量来怀疑误分类。若检测到误分类，SPROUT会阻止分类器输出传播到系统整体。对安全性的影响在于，SPROUT将异常输出（误分类）转化为数据遗漏故障，此类故障可在系统层面轻松处理。SPROUT适用范围广泛，可适配包含图像和表格数据的二分类及多分类任务。实验证明，SPROUT始终能识别监督分类器中的大部分误分类，并在特定情况下可检测所有误分类。SPROUT实现包含预训练封装器，可公开获取，且部署过程极为简便。