  Despite the superior performance in modeling complex patterns to address
challenging problems, the black-box nature of Deep Learning (DL) methods impose
limitations to their application in real-world critical domains. The lack of a
smooth manner for enabling human reasoning about the black-box decisions hinder
any preventive action to unexpected events, in which may lead to catastrophic
consequences. To tackle the unclearness from black-box models, interpretability
became a fundamental requirement in DL-based systems, leveraging trust and
knowledge by providing ways to understand the model's behavior. Although a
current hot topic, further advances are still needed to overcome the existing
limitations of the current interpretability methods in unsupervised DL-based
models for Anomaly Detection (AD). Autoencoders (AE) are the core of
unsupervised DL-based for AD applications, achieving best-in-class performance.
However, due to their hybrid aspect to obtain the results (by requiring
additional calculations out of network), only agnostic interpretable methods
can be applied to AE-based AD. These agnostic methods are computationally
expensive to process a large number of parameters. In this paper we present the
RXP (Residual eXPlainer), a new interpretability method to deal with the
limitations for AE-based AD in large-scale systems. It stands out for its
implementation simplicity, low computational cost and deterministic behavior,
in which explanations are obtained through the deviation analysis of
reconstructed input features. In an experiment using data from a real
heavy-haul railway line, the proposed method achieved superior performance
compared to SHAP, demonstrating its potential to support decision making in
large scale critical systems.
