Abstract The algorithmic fairness literature has produced valuable tools for detecting bias in AI-powered human resource management (HRM) systems. However, these tools rest on an assumption that leaves one deployment configuration analytically under-specified: that bias arises from errors within a shared evaluative framework. As AI-HRM systems designed in Western contexts are applied to the workers whose competence is constituted in a different epistemological tradition, a different form of misrecognition may emerge that cannot be addressed by data augmentation and calibration of fairness metrics. This paper introduces Algorithmic Epistemological Incongruence (AEI) to name that condition. AEI builds upon construct validity theory (Messick, 1995; Cronbach and Meehl, 1955) by shifting the unit of analysis beyond measurement adequacy to evaluative ontology alignment, posing not whether the AI system measures its construct correctly, but whether the construct that it measures is the one that is relevant to the deployment context. Drawing on a theoretically guided synthesis of six bodies of literature, this review develops the Afrocentric Algorithmic Management Framework (AAMF), using African Ubuntu-grounded organizational contexts as its primary analytical site. Three pathways of misrecognition, including Relational Performance Misattribution, Communicative Capital Devaluation, and Historical Stratification Encoding, specify how AEI can give rise to Epistemic Algorithmic Bias (EAB). Three sequential design orientations to each of the pathways are given by the Afrocentric Design Architecture, and the institutional condition that allows their continued operation is provided by Communal Algorithmic Governance. It advances three propositions, three boundary conditions, and a seven question research agenda.