Algorithmic authority and the complexities of delegated decision-making: Case studies on ethical challenges for 21st-century leadership

Abstract

The rapid integration of artificial intelligence (AI) into high-stakes decision-making has outpaced established mechanisms for human oversight and accountability, leaving organisations with limited guidance on the responsible delegation of decision authority. This study examines four widely documented AI deployments: the UK A-Level grading algorithm implemented during the COVID-19 pandemic, Amazon's automated hiring system, the COMPAS recidivism risk assessment tool used in the U.S. criminal justice system, and the Dutch SyRI welfare-fraud detection system. Using 61 publicly available sources, including government reports, organisational documents, and media accounts, we conducted a comparative qualitative analysis based on a two-phase grounded-theory coding approach. The analysis produced a 32-item codebook, which was subsequently applied across 110 coded segments, with quantitative analyses used to assess coding consistency across cases. Four recurring governance principles emerged from the findings: (1) Intentionality, whereby leaders deliberately determine when AI should be used; (2) Interpretability, requiring decision processes to be sufficiently transparent to enable explanation and scrutiny; (3) Moral Authorship, whereby identifiable human actors retain responsibility for delegated decisions; and (4) Justice, requiring delegation arrangements that minimise the reinforcement of existing inequities. These findings contribute an empirically derived framework for examining leadership accountability and AI governance in high-stakes organisational settings.