Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
<|reference_start|>Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD: 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 for seamless integration with industrial Decision Support Systems. We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. These tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments, paving the way for trustworthy and actionable insights in the age of Industry 5.0.<|reference_end|>
@article{zaccaria2024enabling,
    title={Enabling Efficient and Flexible Interpretability of Data-driven Anomaly
  Detection in Industrial Processes with AcME-AD},
    author={Valentina Zaccaria, Chiara Masiero, David Dandolo, Gian Antonio Susto},
    journal={arXiv preprint arXiv:2404.18525},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18525},
    primaryClass={cs.LG}
}