We present a new methodology for handling AI errors by introducing weakly supervised AI error correctors with a priori performance guarantees. These AI correctors are auxiliary maps whose role is to moderate the decisions of some previously constructed underlying classifier by either approving or rejecting its decisions. The rejection of a decision can be used as a signal to suggest abstaining from making a decision. A key technical focus of the work is in providing performance guarantees for these new AI correctors through bounds on the probabilities of incorrect decisions. These bounds are distribution agnostic and do not rely on assumptions on the data dimension. Our empirical example illustrates how the framework can be applied to improve the performance of an image classifier in a challenging real-world task where training data are scarce.

翻译：我们提出了一种处理AI错误的新方法，通过引入具有先验性能保证的弱监督AI错误修正器。这些AI修正器作为辅助映射，通过批准或拒绝先前构建的底层分类器的决策来调节其判断。拒绝决策可被视为建议放弃做出决策的信号。本工作的关键技术焦点在于通过错误决策概率的界限为这些新型AI修正器提供性能保证，这些界限与数据分布无关，且不依赖于数据维度的假设。我们的实证案例展示了该框架如何在训练数据稀缺的挑战性真实任务中提升图像分类器的性能。