  Pursuing fast and robust interpretability in Anomaly Detection is crucial,
especially due to its significance in practical applications. Traditional
Anomaly Detection methods excel in outlier identification but are often
black-boxes, providing scant insights into their decision-making process. This
lack of transparency compromises their reliability and hampers their adoption
in scenarios where comprehending the reasons behind anomaly detection is vital.
At the same time, getting explanations quickly is paramount in practical
scenarios. To bridge this gap, we present AcME-AD, a novel approach rooted in
Explainable Artificial Intelligence principles, designed to clarify Anomaly
Detection models for tabular data. AcME-AD transcends the constraints of
model-specific or resource-heavy explainability techniques by delivering a
model-agnostic, efficient solution for interoperability. It offers local
feature importance scores and a what-if analysis tool, shedding light on the
factors contributing to each anomaly, thus aiding root cause analysis and
decision-making. This paper elucidates AcME-AD's foundation, its benefits over
existing methods, and validates its effectiveness with tests on both synthetic
and real datasets. AcME-AD's implementation and experiment replication code is
accessible in a public repository.
