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.
From Democracies to Autocracies: How AI Systems Enable Authoritarianism by Design
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.
Researchers Detail How AI Systems Can Enable Authoritarianism
A new preprint study of authoritarianism-enabling AI presents another set of evidence of the gaps in safeguards, writes Tim Bernard.
The rise of AI sovereignty: Authoritarian technological imaginaries as a form of reflexive control
Recent literature increasingly examines generative artificial intelligence's (AI's) role in disinformation and propaganda as a key driver of digital authoritarianism. Yet, less attention has been paid to how authoritarian regimes may influence the global governance of AI itself. This essay addresses this gap by analysing the impact of authoritarian technological imaginaries, focusing on the rise of ‘AI sovereignty’ as a central concept shaping global AI governance. It adopts a conceptual framework that combines the notion of imaginaries with the concept of reflexive control, originating in Soviet strategic thought and referring to influencing an adversary's perceptions so they act in ways aligned with the initiator's goals, to study authoritarian diffusion and the global politics of AI. Through an examination of public statements by the Russian president, the study shows how Russia constructs an AI imaginary that extends Cold War logics of nuclear competition into the digital era, framing AI as a matter of security and survival rather than ethics or rights. Drawing on the Russian case, the analysis identifies three frames that underpin the authoritarian AI imaginary: AI as a tool for power and domination; Western AI as a cultural threat; and AI as a guarantor of state efficiency. The article argues that such imaginaries shape the cognitive environment of global policy debates, promoting sovereignty-centred and securitised approaches to AI governance. It suggests that recognising the role of reflexive control in authoritarian diffusion helps distinguish legitimate Global South concerns from authoritarian influence and supports more inclusive and resilient models of AI governance.
The rise of AI sovereignty: Authoritarian technological imaginaries as a form of reflexive control
Recent literature increasingly examines generative artificial intelligence's (AI's) role in disinformation and propaganda as a key driver of digital authoritarianism. Yet, less attention has been paid to how authoritarian regimes may influence the global governance of AI itself. This essay addresses this gap by analysing the impact of authoritarian technological imaginaries, focusing on the rise of ‘AI sovereignty’ as a central concept shaping global AI governance. It adopts a conceptual framework that combines the notion of imaginaries with the concept of reflexive control, originating in Soviet strategic thought and referring to influencing an adversary's perceptions so they act in ways aligned with the initiator's goals, to study authoritarian diffusion and the global politics of AI. Through an examination of public statements by the Russian president, the study shows how Russia constructs an AI imaginary that extends Cold War logics of nuclear competition into the digital era, framing AI as a matter of security and survival rather than ethics or rights. Drawing on the Russian case, the analysis identifies three frames that underpin the authoritarian AI imaginary: AI as a tool for power and domination; Western AI as a cultural threat; and AI as a guarantor of state efficiency. The article argues that such imaginaries shape the cognitive environment of global policy debates, promoting sovereignty-centred and securitised approaches to AI governance. It suggests that recognising the role of reflexive control in authoritarian diffusion helps distinguish legitimate Global South concerns from authoritarian influence and supports more inclusive and resilient models of AI governance.
AI, Geopolitics, and National Security (Chapter 8)
Artificial intelligence is reshaping geopolitics by transforming how states generate, interpret, and project power. Rather than producing a single new weapon, AI operates as a general-purpose technology that scales perception, prediction, and decision-making across military, civilian, and informational domains. This chapter argues that AI rivalry increasingly depends on control over material and organizational foundations, including semiconductors, compute infrastructure, data centers, industrial coordination, and institutional adoption. It examines battlefield integration, U.S.–China competition, semiconductor statecraft, cyber operations, information power, and governance. The chapter shows that AI capabilities diffuse rapidly through commercial platforms, open-source ecosystems, and wartime experimentation, while frontier development remains constrained by infrastructure and supply-chain chokepoints. Because AI capability is difficult to measure or verify, governance is shifting away from traditional arms-control models toward export controls, standards, voluntary commitments, and multistakeholder coordination.
Loss of Oversight: How AI systems may become harder to audit, monitor, and investigate
The safety of advanced AI systems increasingly depends on the ability to oversee them: to audit models for concerning behaviours before deployment, monitor their activity during operation, and investigate incidents after they occur. This report maps the landscape of AI oversight and assesses how it is likely to change. Drawing on 25 expert interviews across frontier AI developers, government, NGOs, and academia, together with a literature review and internal analysis, we examine five sources of oversight signal: model behaviour, chain-of-thought reasoning, internals activations and circuits, memory architectures, and honesty training. For each source, we identify the properties that current oversight relies on, the pathways by which these properties could degrade, and the technical levers available to preserve them. Our central finding is that literature and expert opinion support the conclusion that current oversight rests on foundations that are likely to erode, absent effective intervention. We recommend that developers track and report shifts in oversight-relevant properties, preserve oversight affordances by design, and invest in emerging oversight techniques as fallbacks against continued degradation of current methods.
AI Agents Push Humans Out of the Loop
AI agents pose significant risks as they are granted increasing autonomy. A commonly proposed solution is human oversight and keeping a ''human in the loop'', but this is not a simple solution: Not only do current approaches to AI agent design impede effective human oversight, but the cognitive capacities required for it are also themselves degraded by extended use of AI systems. This position paper argues that current approaches to the development and deployment of AI agent systems do not support effective human oversight -- they contribute to its degradation. To address this, a top priority in the advancement of AI agents should be supporting the situated goals and cognitive requirements of effective human oversight, treating the human needs of overseers at the same level of importance as AI agent capability. To put this idea into practice, we connect work on automation and human-computer interaction to AI agent processes, outlining design-level affordances and organizational protocols that (1) support overseers in exercising critical judgement and (2) counteract the skill atrophy that arises from extended use of automation. We urge developers and deployers to adopt these or similar approaches. Without explicit support for the cognitive demands of effective human-agent interaction, AI agent systems will continue to passively incentivize the degradation of the very human skills they rely on.
AI Agents Push Humans Out of the Loop
AI agents pose significant risks as they are granted increasing autonomy. A commonly proposed solution is human oversight and keeping a ''human in the loop'', but this is not a simple solution: Not only do current approaches to AI agent design impede effective human oversight, but the cognitive capacities required for it are also themselves degraded by extended use of AI systems. This position paper argues that current approaches to the development and deployment of AI agent systems do not support effective human oversight -- they contribute to its degradation. To address this, a top priority in the advancement of AI agents should be supporting the situated goals and cognitive requirements of effective human oversight, treating the human needs of overseers at the same level of importance as AI agent capability. To put this idea into practice, we connect work on automation and human-computer interaction to AI agent processes, outlining design-level affordances and organizational protocols that (1) support overseers in exercising critical judgement and (2) counteract the skill atrophy that arises from extended use of automation. We urge developers and deployers to adopt these or similar approaches. Without explicit support for the cognitive demands of effective human-agent interaction, AI agent systems will continue to passively incentivize the degradation of the very human skills they rely on.
Artificial intelligence in government: why people feel they lose control
The use of Artificial Intelligence (AI) in public administration is expanding rapidly. While AI promises greater efficiency and responsiveness, its integration into government and administration ra...
From chatbots to assistants: governance is key for AI agents
AI's shift into agentic technology ushers in a new set of governance and security challenges that will mean defining to what extent they should be autonomous
Human-Computer Insurrection: Notes on an Anarchist HCI
The HCI community has worked to expand and improve our consideration of the societal implications of our work and our corresponding responsibilities. Despite this increased engagement, HCI continues to lack an explicitly articulated politic, which we argue re-inscribes and amplifies systemic oppression. In this paper, we set out an explicit political vision of an HCI grounded in emancipatory autonomy - an anarchist HCI, aimed at dismantling all oppressive systems by mandating suspicion of and a reckoning with imbalanced distributions of power. We outline some of the principles and accountability mechanisms that constitute an anarchist HCI. We offer a potential framework for radically reorienting the field towards creating prefigurative counterpower - systems and spaces that exemplify the world we wish to see, as we go about building the revolution in increment.
Human-Computer Insurrection: Notes on an Anarchist HCI
The HCI community has worked to expand and improve our consideration of the societal implications of our work and our corresponding responsibilities. Despite this increased engagement, HCI continues to lack an explicitly articulated politic, which we argue re-inscribes and amplifies systemic oppression. In this paper, we set out an explicit political vision of an HCI grounded in emancipatory autonomy - an anarchist HCI, aimed at dismantling all oppressive systems by mandating suspicion of and a reckoning with imbalanced distributions of power. We outline some of the principles and accountability mechanisms that constitute an anarchist HCI. We offer a potential framework for radically reorienting the field towards creating prefigurative counterpower - systems and spaces that exemplify the world we wish to see, as we go about building the revolution in increment.
To Repress or to Co‐opt? Authoritarian Control in the Age of Digital Surveillance
This article studies the consequences of digital surveillance in dictatorships. I first develop an informational theory of repression and co-optation. I argue that digital surveillance resolves dicta...
Whitepaper — The Collective Intelligence Project
I. Capitalist Acceleration: Sacrificing safety for progressII. Authoritarian Technocracy: Sacrificing participation for safetyIII. Shared Stagnation: Sacrificing progress for participation
State Capacity of Secret Surveillance
The state capacity-civil society tradeoff model tends to treat the “state” and “civil society” as separate entities who move to constrain one another. However, this modeling technique leaves out the nuances of individual action within a collective setting by treating each as a relative black box. This article explores this balance in the context of the surveillance state that has arisen in the 20th and 21st century. As state capacity in surveillance increases it better allows the state to respond to threats to citizens from citizens. However, the increased capacity also lessens the ability of societal pressure to check authoritarian advances even in a nation with a thriving civil society presence.
State Capacity of Secret Surveillance
The state capacity-civil society tradeoff model tends to treat the “state” and “civil society” as separate entities who move to constrain one another. However, this modeling technique leaves out the nuances of individual action within a collective setting by treating each as a relative black box. This article explores this balance in the context of the surveillance state that has arisen in the 20th and 21st century. As state capacity in surveillance increases it better allows the state to respond to threats to citizens from citizens. However, the increased capacity also lessens the ability of societal pressure to check authoritarian advances even in a nation with a thriving civil society presence.