FMMO: Detecting the Divergence Between Local Attribution and Global Drift

Abstract:Post-deployment drift poses a critical risk to algorithmic accountability, particularly when ground truth labels are delayed and performance degradation becomes a "silent failure". While Explainable AI (XAI) is often relied upon to audit these shifts, we demonstrate that popular local attribution methods (e.g., TreeSHAP) can exhibit misleading stability even as model reliability collapses. In this paper, we propose a Framework for Model Monitoring and Observability (FMMO) designed to expose the divergence between local explanation stability and global distribution shifts. Using benchmark, synthetic, and real-world datasets, we show that local XAI methods fail to flag drift-induced disparate impact, specifically where False Positive Rates spike for protected groups while feature attributions remain unchanged. By integrating global surrogate models with model utilization measurements, FMMO mitigates this fairness blind spot, ensuring that stakeholders can detect discriminatory deterioration that standard local XAI tools overlook.

Machine Learning (cs.LG)

arXiv:2609.06173 [cs.LG]

(or arXiv:2609.06173v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.06173 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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