Delegated but not absolved: why organizations must reclaim authority in algorithmic decision-making
TL;DR
The paper reframes delegation theory under AI conditions, arguing that the problem sits deeper than governance design, in a fundamental misclassification: algorithmic systems are not delegates in any meaningful organizational sense, and treating them as such removes accountability without removing authority.
Abstract
This study aims to argue that organizations systematically misclassify algorithmic delegation as a variant of conventional human delegation. This misclassification carries direct consequences for organizational risk, legal exposure and decision quality. The paper aims to propose a framework for legitimate delegated authority under artificial intelligence (AI) conditions. Drawing on organizational theory, AI governance scholarship and documented organizational cases, the paper identifies six characteristics that distinguish algorithmic authority from human delegation and proposes five criteria managers must address before deploying consequential algorithmic systems. Organizations that treat algorithmic deployment as an efficiency upgrade, when it is really an authority transfer, create accountability vacuums that expose them to reputational, legal and operational risk. Governance frameworks built around transparency, auditability, scope limits, human override and value alignment can close that gap. Managers deploying algorithmic systems in hiring, performance management, resource allocation or customer decisions face five governance questions they must answer before deployment, not after failure. The paper provides these as a structured checklist with supporting rationale. The paper reframes delegation theory under AI conditions, arguing that the problem sits deeper than governance design, in a fundamental misclassification: algorithmic systems are not delegates in any meaningful organizational sense, and treating them as such removes accountability without removing authority.