[r/ML] [P] Using SHAP to explain Unsupervised Anomaly Detection on PCA-anonymized data (Credit Card Fraud). Is this a valid approach for a thesis?

Summary

A BSc student is developing a thesis on explainable AI (XAI) for credit card fraud detection, using SHAP to interpret an unsupervised anomaly detection model (Stacked Autoencoder) trained on the Kaggle dataset. Fraud is identified by high reconstruction error. A key concern for the student is the validity of applying SHAP explanations when the dataset features are PCA-transformed and anonymized, and they are seeking community feedback on this approach for their dissertation.

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