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。数据分析师必须确保运行中的数据与训练阶段一致，同时理解环境是否发生了可能使自主决策不再安全的变化。本文方法基于可解释人工智能技术，通过考虑不同度量指标来识别分布内数据与分布外数据在被XAI模型观测时的任何相似性。该方法无需参数假设且不依赖分布假设。在复杂场景（预测性维护、车辆编队、网络安全中的隐蔽通道）上的验证结果，既证实了检测精度，也证实了训练-运行条件接近性评估的有效性。相关结果可通过开源代码与开源数据获取，链接如下：https://github.com/giacomo97cnr/Rule-based-ODD。