Summary

While the advent of AI has pushed the boundary of what’s possible, interpretability makes it more accessible by providing relevant model metrics to satisfy the needs of company stakeholders. Future XAI advancements require further testing on the validity of existing explainability frameworks and careful engineering to communicate human-comprehensible explanations and drive effective interpretability.

This chapter concludes Part 1 of the book. You gained an overview of deep learning and XAI for anomaly detection, including their significance and best practices. In Part 2, we will cover practical examples of building deep learning anomaly detectors, starting with NLP.