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October 27, 2025

Toward sustainable AI leadership: ethical blind spots, accountability gaps and the CARE governance framework

Key Points

Conceptual analysis identifies power dynamics in AI leadership, highlighting critical blind spots.
Key findings emphasize the role of interdisciplinary literature in understanding responsibility in AI governance.
The proposed CARE framework provides a structured approach to tackle accountability gaps in leadership.
Implications suggest a need for ethical oversight in AI systems to enhance trust and accountability.

Abstract

Purpose This conceptual paper addresses the growing concern of leadership accountability and power asymmetries in AI-integrated organizations. It proposes a structured framework to navigate the ethical, institutional and technical blind spots that often go unnoticed in AI governance. Design/methodology/approach Drawing on interdisciplinary literature from leadership studies, AI ethics and organizational governance, the paper synthesizes insights to propose a novel theoretical framework (“CARE”) for responsible AI oversight. Findings The study identifies three dimensions of leadership negligence, technical, ethical and institutional, that create accountability gaps and obscure power dynamics in AI systems. The CARE Framework (Control, Awareness, Responsibility and Evaluation) guides leaders in assessing and addressing these gaps across different sectors. Originality/value This paper contributes a practical, theory-informed model for ethical AI leadership and provides a foundation for future empirical studies. It bridges fragmented discussions across disciplines and informs both scholars and practitioners aiming to implement trustworthy, power-aware AI systems.

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