Artificial Intelligence, Public Policy and Governance - implications for Economic Management and Political Systems
Glory Mmerechi Triumph Okereke, Philip Williams Appiah-Agyei
Journal of Scientific Research and Reports · pp. 828–840 · Published 20 Apr 2026
10.9734/jsrr/2026/v32i44140Abstract
As a General-Purpose Technology (GPT), Artificial Intelligence (AI) is fundamentally reconfiguring state capacity, as well as the mechanics of global economic management. This systematic review examines current research studies (2018-2026) to assess the socio-political consequences of artificial intelligence-driven governance in three key dimensions - policy integration, economic consequences and democratic legitimacy. Following the guidelines of the Preferred Reporting of Items in a Systematic Review and Meta-Analysis (PRISMA), the outcomes of this review show a structural shift from "street level" bureaucracies to "system-level" architectures that can be defined as the institutional division of "Artificial Discretion" to algorithmic infrastructures - with empirical evidence showing great gains in efficiency at routinised administrative tasks and fiscal forecasting, offsets by a growing "Efficiency-Legitimacy Paradox". The findings reveal the importance of the "black box" nature of automated systems, epitomised by the Australian 'Robo-debt' scandal, that undermines the democratic social contract and principles of procedural justice. Furthermore, the synthesis presents a stark geopolitical divide between the "AI Core" nations and the Global South; the latter group faces acute risks of "Digital Dependency" as well as eroded digital sovereignty. In order to alleviate these types of tensions, the review examines the effectiveness of visibility mechanisms, such as public algorithm registers or role-sensitive explainability, in regaining citizen trust. The study concludes that the sustainability of the algorithmic state rests on a movement from technocratic secrecy to value-based transparency that will ensure that AI- and human collaboration is founded on institutional accountability and algorithmic justice.
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