Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is non-parametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity. Results are available via open source and open data at the following link: https://github.com/giacomo97cnr/Rule-based-ODD.

翻译：分布外检测是机器学习部署中最关键的问题之一。数据从业者必须确保运行中的数据符合训练阶段的要求，同时理解环境是否已发生改变，使得自主决策不再安全。本文的方法基于可解释人工智能；它考量多种度量指标，以识别由XAI模型观察到的分布内与分布外数据之间的任何相似性。该方法是非参数的且无需分布假设。在复杂场景（预测性维护、车辆编队、网络安全中的隐蔽信道）上的验证，证实了检测的精确性以及对训练-运行条件接近程度的评估。结果可通过开源代码和开放数据在以下链接获取：https://github.com/giacomo97cnr/Rule-based-ODD。