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arXiv AI By Marco Zullich, Emily Schiller, Ivan Gentile, David Dembinsky, Adriano Lucieri, Kilian G\"oller, Steffen Seitz

Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance

Read the original on arXiv AI →

The paper argues that explainable AI (XAI) tools, intended to increase trust in machine learning models, often fail to do so and instead foster overreliance on incorrect predictions. It attributes this failure to the unfaithfulness of XAI methods, which oversimplify model internals and misalign explanations with the actual model behavior. The authors identify five major shortcomings in current faithfulness metrics and propose four research directions to improve their reliability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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