From Democracies to Autocracies: How AI Systems Enable Authoritarianism by Design

2026-06-15

AI Summary

This study investigates how artificial intelligence systems, through their design and operational mechanisms, can facilitate authoritarian practices across diverse political regimes, including both democracies and autocracies. Drawing on a systematic comparative analysis of AI systems across six countries and synthesizing multi-source qualitative data—including academic literature, government documents, and media reports—the research demonstrates that authoritarianism is not exclusive to autocratic states but emerges from distributed technological and governance choices throughout the AI lifecycle. The study identifies four key mechanisms: centralization of administrative data for law enforcement and political sanctioning, regulatory gaps, diminished human oversight due to user compliance, and the encoding of attributes associated with marginalized groups to enable targeted identification. Findings reveal that both centralized and fragmented AI architectures can reinforce authoritarian logics when exploited through governance vulnerabilities.

Abstract

AI-enabled authoritarianism is not confined to autocracies. In this paper, we provide greater transparency by investigating and mapping the lifecycles of six AI systems deployed in different political regimes, ranging from the US to China. By drawing on an extensive range of sources (academic publications, investigative research reports, third-party evaluations, media interviews, government procurement notices), we conduct a systematic, qualitative comparison across systems to identify the critical technical and operational features that enable authoritarianism within their respective political contexts. We find that enabling features include the centralization and co-optation of administrative data for law enforcement and political punishment, regulatory gaps that fail to deter misuse, weak user compliance that nullifies human oversight mechanisms, and the encoding of protected group traits that identify members of vulnerable populations. We find that these features are present across systems deployed in autocratic and democratic regimes, albeit in varying configurations. We also find that both centralized and fragmented AI systems can contribute to authoritarianism by exploiting governance gaps: centralized systems directed by executive authorities, particularly within security and military institutions, are often not subjected to formal oversight mechanisms, while fragmented systems diffuse accountability between stakeholders, paving the way for entrenchment. These findings reveal that AI-enabled authoritarianism is distributed, resulting from design and operational choices made by developers, administrators, and users alike. We conclude with recommendations for developers and policymakers to mitigate these risks.

Problem

• AI-enabled authoritarianism
• governance gaps
• centralization
• regulatory gaps
• vulnerable populations

Innovation

• AI-enabled authoritarianism
• system lifecycle analysis
• governance gaps
• data centralization
• algorithmic accountability