Rule-based Out-Of-Distribution Detection
It addresses safety and compliance issues for data analysts and practitioners in real-world applications, though it appears incremental as it builds on existing XAI techniques.
The paper tackles the problem of out-of-distribution detection in machine learning deployment by proposing a rule-based method using explainable AI, validated in scenarios like predictive maintenance and cybersecurity with high precision.
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.