Auditable AI: Metacognitive Calibration, Algorithmic Accountability, and the Engineering of Trustworthy Organizational Intelligence

Abstract

The deployment of artificial intelligence systems across organizational life has generated a fundamental accountability gap: AI models of increasing complexity and capability are making or influencing decisions that carry significant consequences for individuals and institutions, yet the tools and frameworks available for auditing these systems have not kept pace with the technology's rapid advancement. This accountability gap is compounded by a calibration crisis: state-of-the-art AI systems, including large language models, systematically misrepresent their own reliability, producing confident predictions in situations where their actual performance is poor. This paper develops a comprehensive framework for auditable AI in organizational settings, integrating three critical but previously disconnected research threads: the metacognitive calibration challenge illuminated by the MIRROR benchmark, the structural constraints on AI auditing identified by the Verification Tax analysis, and the practical demand for explainability and accountability in human resource analytics. Drawing on three foundational references and twelve supplementary citations spanning AI risk management frameworks, organizational trust theory, and algorithmic accountability science, this study proposes a practical architecture for building, deploying, and governing AI systems that are not only accurate but auditable, trustworthy, and accountable. The framework addresses the full AI lifecycle from design through deployment and post-market surveillance, with particular attention to the governance mechanisms that enable meaningful human oversight. The findings indicate that auditable AI is not merely a regulatory compliance requirement but a competitive differentiator and organizational capability that will increasingly determine which institutions can be trusted to deploy AI responsibly.