Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD

arXiv:2404.18525v1 Announce Type: new
Abstract: While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. This paper addresses this need by testing the applicability of AcME-AD in industrial settings. This recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. Thus, it seems suitable …

abstract anomaly anomaly detection arxiv become challenge cs.lg data data-driven decision detection enabling focus human human-centric industrial industry industry 4.0 insights interpretability machine machine learning nature paper processes transformation trust type while