[Submitted on 24 Sep 2026]
Title:Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance
View a PDF of the paper titled Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance, by Marco Zullich and 6 other authors
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Abstract:Explainable Artificial Intelligence (XAI) tools have often been presented as methods to increase trust in machine learning models by making aspects of their algorithms more human-understandable. However, evidence accumulated over the last decade points to an overall failure of this objective, instead promoting overreliance on incorrect predictions, which can subsequently lead to algorithmic aversion. Rather, XAI appears to function primarily as a debugging tool, useful for obtaining anecdotal insights into, for example, features learned by the model. One of the main issues underlying this limitation is that XAI methods often oversimplify the inner workings of the model, resulting in misalignment between the explanation and the model itself - a behavior referred to as unfaithfulness or infidelity. Faithfulness can be interpreted as the degree of approximation between an explanation and the model. Computing faithfulness in a rigorous manner would thus make it possible to quantify one of the various elements of trustworthiness of an explanation. However, existing metrics present significant limitations, making this quantification either infeasible or computationally expensive. In this position paper, we outline five major shortcomings of current faithfulness metrics and propose four possible avenues for research to bolster their reliability.
Comments: Presented at the IJCAI-ECAI 2026 Workshop on Explainable Artificial Intelligence (XAI)
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2610.12493 [cs.AI]
(or arXiv:2610.12493v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.12493
arXiv-issued DOI via DataCite
Submission history
From: Marco Zullich [view email] [v1] Thu, 24 Sep 2026 15:29:40 UTC (111 KB)
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