  Anomaly detection algorithms are often thought to be limited because they
don't facilitate the process of validating results performed by domain experts.
In Contrast, deep learning algorithms for anomaly detection, such as
autoencoders, point out the outliers, saving experts the time-consuming task of
examining normal cases in order to find anomalies. Most outlier detection
algorithms output a score for each instance in the database. The top-k most
intense outliers are returned to the user for further inspection; however the
manual validation of results becomes challenging without additional clues. An
explanation of why an instance is anomalous enables the experts to focus their
investigation on most important anomalies and may increase their trust in the
algorithm.
  Recently, a game theory-based framework known as SHapley Additive
exPlanations (SHAP) has been shown to be effective in explaining various
supervised learning models. In this research, we extend SHAP to explain
anomalies detected by an autoencoder, an unsupervised model. The proposed
method extracts and visually depicts both the features that most contributed to
the anomaly and those that offset it. A preliminary experimental study using
real world data demonstrates the usefulness of the proposed method in assisting
the domain experts to understand the anomaly and filtering out the
uninteresting anomalies, aiming at minimizing the false positive rate of
detected anomalies.
