Summary
After reviewing XAI evaluation schemes and benchmarking metrics in this chapter, you now understand how to analyze qualities of explanations based on NIST’s fundamental XAI principles.
This chapter brings us to the end of the book. Thank you for staying with me through the journey to explore various XAI topics. We studied XAI’s challenges, opportunities, and significance in deep learning anomaly detection. You learned about building explainable deep learning models in detecting anomalies in NLP, time series, and computer vision by integrating theory and practice throughout the book. You now understand how to quantify and assess model explainability to meet regulatory compliance and mitigate bias to ensure fairness and ethical analysis.
As we often hear, correlation does not imply causation. It is challenging to identify causality based on observations without performing controlled experiments. XAI systems aim to facilitate reasoning in ML algorithms and...