Explainable AI (XAI) for Anomaly Detection: Understanding Outlier Predictions

Photo by Christophe Maertens on Unsplash

Explainable AI (XAI) for anomaly detection focuses on enhancing the interpretability of models designed to identify outliers or anomalies in data. Understanding why a model flags certain instances as anomalies is crucial for decision-making and building trust in anomaly detection systems. Let’s explore key concepts, techniques, and considerations for XAI in the context of anomaly detection.

I. Key Concepts:

1. Anomaly Detection:

• Anomaly detection involves identifying patterns in data that deviate significantly from the norm. XAI aims to provide insights into why a specific instance is flagged as an anomaly.

2. Feature Importance for Anomalies:

• Determining which features contribute most to the identification of anomalies is essential for understanding the decision-making process of an anomaly detection model.

3. Local vs. Global Interpretability:

• Local interpretability focuses on understanding the reasons behind individual predictions, while global interpretability provides insights into the overall behavior of the model across the entire dataset.