Legitimacy Under Algorithmic Authority: A Relational Diagnostic of Leadership Education in AI‐Mediated Contexts

Abstract

As algorithmic systems become embedded in leadership development, evaluation, and recognition, they increasingly shape how authority is formed and justified. This manuscript argues algorithmic authority poses a legitimacy challenge for leadership education, especially when judgment is co‐produced by humans and systems. It introduces the Surface–Cultural–Institutional–Systemic (SCIS) framework as a diagnostic lens for examining how legitimacy is configured, misaligned, or strained within hybrid authority systems. Rather than prescribing AI use or attributing legitimacy to algorithmic systems, I position leadership education as a formative but bounded site for cultivating conceptual readiness to interpret legitimacy as relational, contested, and mediated.