Artificial Intelligence as Organizational Agent: Legal Standing, Accountability Gaps, and the Governance of Algorithmic Decision-Making Authority

Abstract

The deployment of artificial intelligence systems in consequential organizational decisions has created a fundamental legal and organizational challenge: the accountability gap. When an AI system makes or materially influences a decision that causes harm—an discriminatory hiring outcome, a negligent medical recommendation, a faulty financial assessment—the existing frameworks of legal responsibility and organizational accountability provide no clear answer to the question of who is responsible. This paper develops a comprehensive analysis of AI as an organizational agent, examining the legal, organizational, and governance dimensions of the accountability gap and the institutional innovations required to address it. Drawing on three foundational references and twelve supplementary citations spanning legal theory, AI governance, organizational law, and regulatory design, this study argues that the accountability gap is not merely a legal technicality but a structural feature of how AI systems function as de facto agents within organizational hierarchies. The analysis reveals that the metacognitive miscalibration documented by the MIRROR benchmark, the structural constraints on AI auditing identified by the Verification Tax, and the practical demand for explainability in human resource analytics illuminate different dimensions of the same underlying phenomenon: AI systems exercise genuine agency—the capacity to make consequential decisions—without the accountability structures that the exercise of agency has historically required. The paper concludes by proposing an Algorithmic Agency Governance Framework that addresses the accountability gap through a combination of liability reform, organizational governance requirements, and institutional innovations in how AI agency is constituted and overseen.