From predicting dissent to programming power; analyzing AI-driven authoritarian governance in the Middle East through TRIAD framework
This paper examines the strategic adoption of artificial intelligence (AI) by authoritarian leaning regimes in the Middle East, focusing on Iran, Saudi Arabia, and the United Arab Emirates (UAE). By analyzing the commonalities and divergences in AI-driven governance practices across these states, the study explores the broader implications of AI on authoritarian control. Employing an interdisciplinary theoretical approach, it traces how AI-powered technologies such as facial recognition, predictive policing, and digital censorship have transformed surveillance into an adaptive and pervasive mechanism of state control. The study highlights the dual nature of these technologies, demonstrating their capacity to enhance governance efficiency while simultaneously reinforcing political repression. To conceptualize the evolving landscape of digital governance, the paper, drawing on comparative case studies from the Middle East, introduces the TRIAD Framework (Technology, Regulation, Influence, and Adaptive Dynamics). This novel analytical model elucidates the complex interplay between technological advancements, regulatory changes, and geopolitical influence, thereby contributing to the understanding of how these factors sustain authoritarian resilience within the region. The framework illustrates how AI not only fortifies existing mechanisms of control but also fosters a feedback loop wherein technological innovations drive regulatory evolution and reinforce autocratic adaptability.
Authors
- Arash Beidollahkhani (ORCID: https://orcid.org/0000-0002-8295-7142)
Institutions
- Center for Global Development (US)
- University of Manchester (GB)
- Total (Germany) (DE)
- Hannah-Arendt-Institut für Totalitarismusforschung e.V. an der TU Dresden (DE)
Publication Details
- Journal
- Democratization
- Published
- 2025-11-11
- DOI
- https://doi.org/10.1080/13510347.2025.2576527
- Citations
- 1
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 2.37