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.
