Governing Algorithmic Power through Adaptive Legal Frameworks and Institutional Accountability

Abstract

The expanding influence of algorithmic systems in governance, commerce, public administration, and social regulation has created unprecedented transformations in how decisions are made, risks are distributed, and rights are exercised. As artificial intelligence systems acquire increasingly autonomous decision-making capabilities, the distribution of power between institutions, individuals, and automated processes becomes more opaque and difficult to regulate. This paper examines the problem of algorithmic power from a legal and institutional perspective, arguing that traditional regulatory structures are insufficient for governing systems that evolve adaptively, generate socio-technical externalities, and shape social outcomes at scale. The paper explores the foundations of adaptive legal frameworks as a means of managing algorithmic complexity and maintaining democratic accountability. It further analyzes how institutional governance models must evolve to ensure transparency, fairness, and oversight in environments dominated by data-driven and self-improving machine intelligence.