Explainable AI Governance in Organizational Decision-Making: Metacognitive Calibration, Algorithmic Accountability, and the Future of Human-AI Collaboration

Abstract

The rapid integration of artificial intelligence into organizational life has created a governance crisis that spans strategic management, human resource analytics, and the fundamental nature of human-machine collaboration. As AI systems assume an increasingly consequential role in decisions that shape careers, resource allocation, and strategic direction, the capacity of these systems to explain their reasoning—and the capacity of organizations to audit and govern them—has become a central challenge for both research and practice. This paper develops a comprehensive framework for explainable AI governance in organizational decision-making contexts, drawing upon four foundational references and eleven additional citations spanning metacognitive calibration, AI auditing theory, large language model governance, algorithmic management, and human-AI collaboration science. The analysis reveals that current AI governance frameworks are undermined by a fundamental tension: the most capable AI systems are also the most opaque, and the most consequential organizational decisions are precisely those for which transparent, auditable AI is most urgently needed. The paper introduces a governance architecture grounded in metacognitive calibration principles, algorithmic accountability mechanisms, and adaptive trust frameworks that together provide a pathway toward responsible AI deployment in organizations. The framework addresses the distinct requirements of strategic business decision support, human resource analytics, and cross-functional AI oversight, while engaging with the resource constraints identified by the AI auditing literature.