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

arXiv: https://arxiv.org/abs/2606.17286 · HTML: https://mlsanity.com/paper/2606.17286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 147 references that are in the map.
- First posted on arXiv 15 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023)
- [Human-AI Interactions in Public Sector Decision-Making: "Automation Bias" and "Selective Adherence" to Algorithmic Advice](https://mlsanity.com/paper/2103.02381/) (2021)
- [Evidence of What, for Whom? The Socially Contested Role of Algorithmic Bias in a Predictive Policing Tool](https://mlsanity.com/paper/2405.07715/) (2024)

## Abstract (arXiv)

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.