Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI

Anomaly Detection AD is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rationale behind model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest EIF AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over 90% of average precision on the benchmarks considered in the study and outperforms state-of-the-art Explainable Artificial Intelligence XAI approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations.