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

arXiv: https://arxiv.org/abs/2404.18525 · HTML: https://mlsanity.com/paper/2404.18525/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery](https://mlsanity.com/paper/2102.11848/) (2021)
- [Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection](https://mlsanity.com/paper/1909.03495/) (2019)
- [AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box](https://mlsanity.com/paper/2112.12635/) (2021)
- [AcME-AD: Accelerated Model Explanations for Anomaly Detection](https://mlsanity.com/paper/2403.01245/) (2024)

## Built on by (most cited first, 2 of 2)

- [Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection](https://mlsanity.com/paper/2405.15370/) (2024)
- [Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI](https://mlsanity.com/paper/2405.01158/) (2024)

## Abstract (arXiv)

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.