Paper Title
ETHICAL LEADERSHIP IN AI-AUGMENTED ORGANIZATIONS: A STRUCTURAL MODEL OF TRUST AND ACCOUNTABILITY IN ENHANCING DECISION QUALITY AND EMPLOYEE BEHAVIORAL OUTCOMES

Abstract
The rapid proliferation of artificial intelligence (AI) in organizational decision-making has introduced profound challenges for leadership, trust, and accountability. Despite growing scholarly attention to AI adoption and its organizational consequences, the mechanisms through which leadership shapes the relational and cognitive dimensions of human–AI interaction remain theoretically underdeveloped. Drawing on Social Learning Theory and the Technology Acceptance Model, this paper advances a structural theoretical model in which ethical leadership moderates the relationship between AI use and employee trust — encompassing both trust in AI systems and trust in leadership — with downstream effects on decision quality and employee behavioral outcomes. We propose seven formal propositions and develop a conceptual model that situates ethical leadership as a critical boundary condition governing whether AI augmentation strengthens or undermines employee trust within organizations. The model contributes to the literatures on ethical leadership, organizational trust, human–AI collaboration, and responsible AI governance. Implications for theory, management practice, and future empirical research are discussed. Keywords - Ethical Leadership, Artificial Intelligence, Employee Trust, Trust in AI, Decision quality, behavioral Outcomes, Social Learning Theory, Technology Acceptance Model, AI Governance