Who Legitimizes the AI Algorithm? Leadership, Volatility, and the Governance of Algorithmic Authority

Abstract: Artificial intelligence systems increasingly function as decision-making infrastructures that allocate access, classify individuals, and distribute life chances at organizational scale. While ethical AI governance frameworks proliferate, they overwhelmingly assume stable conditions: reliable infrastructure, coherent regulatory institutions, and baseline organizational legitimacy. This article reconceptualizes ethical AI governance as a legitimacy production challenge rather than a technical compliance problem, arguing that under conditions of volatility—infrastructural fragility, institutional flux, and contested social consent—principles and documentation alone cannot sustain governable algorithmic authority. Drawing on legitimacy theory, leadership scholarship, and algorithmic accountability research, the article develops a three-dimensional volatility typology and proposes the Sensing–Stabilizing–Legitimizing capability framework. This leadership-centered model specifies how organizations build contestability, accountability, and procedural justice into AI systems when background stability conditions fail. The contribution is integrative-conceptual: theorizing volatility as an explicit governance variable and positioning ethical AI governance as strategic leadership capability rather than delegated technical function.

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