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

Reddit r/MachineLearning / 3/16/2026

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Key Points

  • The post discusses a BSc dissertation on explainable AI for fraud detection that uses SHAP to explain an unsupervised anomaly detection model (a stacked autoencoder) applied to the Kaggle Credit Card Fraud dataset with PCA-transformed features.
  • A key concern is that since the features are PCA components (e.g., V14, V17), the explanations would be about abstract components rather than easily interpretable real-world factors like location.
  • The author has implemented a custom SHAP explanation aimed at the reconstruction error (mean squared error) of the autoencoder, raising questions about the validity and usefulness of such explanations.
  • The post asks whether abstract interpretability based on PCA-transformed features is a legitimate contribution for a thesis and whether PCA transformation undermines the value of XAI explanations.
  • Overall, the discussion centers on methodology, interpretability, and the scholarly merit of applying SHAP to unsupervised anomaly detection with PCA-reduced features in fraud detection.

Hello everyone,

I’m currently working on a project for my BSc dissertation focused on XAI for Fraud Detection. I have some concerns about my dataset and I am looking for thoughts from the community.

I’m using the Kaggle Credit Card Fraud dataset where 28 of the features (V1-V28) are the result of a PCA transformation.

I am using an unsupervised approach by training a Stacked Autoencoder and fraud is detected based on high Reconstruction Error.

I am using SHAP to explain why the Autoencoder flags a specific transaction. Specifically, I've written a custom function to explain the Mean Squared Error (reconstruction error) of the model .

My Concern is that since the features are PCA-transformed, I can’t for example say "the model flagged this because of the location". I can only say "The model flagged this because of a signature in V14 and V17"

I would love to hear your thoughts on whether this "abstract Interpretability" is a legitimate contribution or if the PCA transformation makes the XAI side of things useless.

submitted by /u/LeaveTrue7987
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