Why AI Risk Looks Different in Different Places
Public debates about artificial intelligence are often presented as if the world is talking about one shared problem. Policy briefs speak of “global challenges,” and news reporting treats AI risk as a universal concern. Yet risk is not a neutral category. Societies interpret technological danger through the political, institutional, and cultural frameworks they already use to make sense of the world. What counts as a threat in one region can seem speculative or marginal in another.
This matters for AI governance because policymaking does not occur in an abstract vacuum. Regulatory frameworks, public expectations, and media attention are shaped by different conceptions of what the central problems are. Understanding these variations is part of understanding the “algorithmic society” itself.
The United States: existential stakes and agentic systems
In the United States, AI risk is frequently framed in existential or agentic terms: runaway optimisation, superintelligent systems, and the possibility that a misaligned artificial agent could overpower human decision-making. These ideas are not broadly distributed across American society; they are amplified by a small number of influential epistemic communities clustered around Silicon Valley, Effective Altruism, rationalist forums, and high-visibility founders.
These groups are disproportionately represented in US media coverage. Their interviews, op-eds, and philanthropic influence make AGI-centric concepts—superintelligence, catastrophic misalignment, extinction risk—more visible than their actual prevalence in technical research or public opinion. Mainstream outlets often quote these voices when covering AI developments, even when they aim to question or critique them.
The result is a public discourse where “AI risk” often defaults to questions about AGI, automation run amok, or catastrophic futures. This framing shapes what policymakers think the public is concerned about and what kinds of solutions are deemed urgent. Safety labs, model evaluations, and alignment research dominate headlines, while governance debates about labour, surveillance, or platform power receive less attention relative to their material impact.
Europe: governance failures and institutional harms
European discussions follow a different trajectory. AI risk is most often described as a problem of governance: bias, discrimination, misleading outputs, opacity, surveillance, labour disruption, and threats to democratic institutions. The EU’s regulatory tradition anchors this framing. Discussions about AI naturally link to the rule of law, due process, rights protection, and institutional safeguards.
The EU AI Act is a reflection of this approach. Rather than imagining AI as an autonomous agent that might “break free,” EU institutions focus on the systems people already use: workplace monitoring tools, biometric identification, recommender systems, automated decision-making in public services. European media coverage follows the same pattern. The dominant risks are structural, not existential.
This contrast does not imply that Europeans are unconcerned about future-oriented scenarios. Instead, the public discourse prioritises harms that are already observable or foreseeable within existing political and economic structures. Where US narratives often focus on systems gaining too much autonomy, European narratives focus on institutions holding too little accountability.
Why these differences matter for AI governance
These divergences have practical implications.
First, they shape policy agendas. If US debate centres existential risk, policymakers become more sensitive to catastrophic-scenario arguments. If European debate centres governance failures, regulation emphasises transparency, oversight, and institutional safeguards.
Second, they affect how governments and regulators interpret industry claims. Companies based in the US increasingly frame their products as powerful enough to require specialised “alignment” processes. European regulators, by contrast, ask for documentation, compliance assurance, and risk assessments. Each region is responding to a different definition of the problem.
Third, these narratives influence the development of the systems themselves. Training data, model evaluations, safety protocols, and risk registers embed assumptions about what counts as harm. If developers in different regions internalise different narratives, the resulting systems may handle uncertainty and alignment differently.
Fourth, cross-regional collaboration becomes harder when actors mean different things by “AI safety.” Without a shared conceptual baseline, international governance efforts can drift or stall. Interoperability is not only a technical question but a discursive one.
Why students and practitioners should pay attention
For those working in AI governance—or preparing to—these narrative differences are not academic details. They shape:
how policymakers justify interventions
which risks receive funding and regulatory traction
how companies frame compliance obligations
what the public believes is at stake
what kinds of expertise are considered relevant
Understanding regional framings helps future practitioners navigate transatlantic policy conversations, evaluate claims made by developers, and interpret risk assessments with more precision. It also highlights the need to distinguish between material harms and narrative-driven concerns.
As algorithmic systems become embedded in public administration, security, welfare provision, and everyday life, governance will require attention to both the technical system and the discursive environment that defines its risks.
Toward a grounded understanding of AI risk
AI governance is often described as a global challenge, but the underlying narratives are far from uniform. Recognising these differences is the first step toward building governance structures that respond to real harms rather than imported imaginaries.
For students, researchers, and practitioners in the Algorithmic Society community, this is an opportunity to rethink what “AI risk” means in practice and how the stories we tell shape the systems we build and regulate.