Algorithmic Accountability and Administrative Decision-Making: Legal Implications of Automated Governance Systems

Abstract

The increasing adoption of artificial intelligence (AI) and algorithmic decision-making systems has transformed public administration by enhancing efficiency, consistency, and predictive capacity in governmental decision-making processes. However, the growing reliance on automated governance systems has also generated significant legal and ethical concerns regarding transparency, accountability, due process, and the protection of fundamental rights. This article examines the legal implications of algorithmic accountability in administrative decision-making through a normative juridical approach employing doctrinal and comparative legal analysis. The study analyzes constitutional principles, administrative law doctrines, data protection regulations, and emerging AI governance frameworks to evaluate the extent to which existing legal systems can ensure accountability for automated administrative decisions. The findings reveal that although algorithmic systems improve administrative efficiency, their opaque decision-making processes, potential algorithmic bias, lack of explainability, and limited mechanisms for human oversight may undermine the principles of legality, procedural fairness, and democratic accountability. The study argues that effective automated governance requires a human-centered legal framework integrating transparency, explainability, independent oversight, risk-based regulation, and accessible judicial remedies. It proposes an Algorithmic Accountability Framework that combines administrative law principles with emerging AI governance standards to ensure that automated decision-making remains consistent with the rule of law and the protection of fundamental rights. This framework contributes to the evolving discourse on responsible AI governance and digital administrative law.