论文标题
模型不可知的本地解释拒绝
Model Agnostic Local Explanations of Reject
论文作者
论文摘要
基于机器学习的决策系统在安全关键领域的应用需要可靠的高确定性预测。拒绝选项是确保系统做出的预测足够高的确定性的常见方法。虽然能够拒绝不确定的样本很重要，但能够解释为什么拒绝特定样本也很重要。但是，解释一般拒绝选项仍然是一个空旷的问题。我们提出了一种模型不可知论方法，用于本地通过可解释的模型和反事实解释来解释任意拒绝选项。
The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the system. While being able to reject uncertain samples is important, it is also of importance to be able to explain why a particular sample was rejected. However, explaining general reject options is still an open problem. We propose a model agnostic method for locally explaining arbitrary reject options by means of interpretable models and counterfactual explanations.
