?asOf= parameter to see the current catalog state.Systemic Risk (AI)
systemic-risk · Risk classification
A regulatory designation indicating that a general-purpose AI model poses risks of significant scale or scope across the EU internal market, triggering Article 55 obligations under the EU AI Act.
Definition & scope
Article 51 of the EU AI Act establishes that a general-purpose AI (GPAI) model has systemic risk when its capabilities equal or exceed those of the most advanced models, evaluated via Annex XIII criteria. Presumption thresholds: ≥10²⁵ FLOPs training compute OR ≥45M EU monthly active users OR designation by the AI Office based on capability indicators. Designation triggers Article 55 obligations: model evaluation including adversarial testing, systemic risk assessment, incident reporting, cybersecurity protection, and energy reporting.
Locus of dispute: EU AIA's systemic-risk thresholds presume that capabilities ≥10²⁵ FLOPs OR ≥45M EU MAU correlate with systemic risk. Field is divided on whether either correlation is empirically validated; the catastrophic-risk literature uses a stricter definition (CBRN uplift, autonomous replication) that the EU AIA does not directly target.
Use in governance
How instruments operationalise this concept
| Instrument | Jurisdiction | Status |
|---|---|---|
| EU AI Act | EU | in force |
| G7 Hiroshima AI Process Code of Conduct | G7 | in force |
| Council of Europe Framework Convention on AI | council_of_europe | adopted not in force |
Appears in topic articles
Editorial note
'Systemic risk' under the EU AIA is distinct from financial-system 'systemic risk' (SIFI/G-SIB regimes). Wiki articles in AI contexts default to the EU AIA usage.
See also
Further reading
Sources on the broader topics this concept relates to — complementing, not standing in for, the primary sources cited inline above. 68 academic & grey-literature sources; catalogued metadata with a primary link; one-line findings are ✦ AI-generated summaries, labeled as such (charter §7.9). Browse the full literature index.
- An interdisciplinary account of the terminological choices by EU policymakers ahead of the final agreement on the AI Act: AI system, general purpose AI system, foundation model, and generative AI Peer-reviewed✦ AITraces how the AI Act's legal text shifted across versions among the terms 'AI system, general purpose AI system, foundation model, and generative AI', exposing definitional instability in the regime.
- The EU model of AI governance: regulating artificial intelligence through law and policy Peer-reviewed✦ AIAnalyses how the AI Act's risk-based model handles general-purpose and foundation models whose 'autonomous content generation challenges legal categories of authorship, accountability, and control'.
- Generative AI and data protection Peer-reviewed✦ AIExamines friction between foundation-model training and the GDPR, noting models that 'memorize and leak pieces of training data' cannot be treated as anonymous.
- Defending Compute Thresholds Against Legal Loopholes Preprint✦ AIIdentifies 'enhancement techniques that are capable of decreasing training compute usage while preserving... model capabilities', exposing loopholes in compute-reporting thresholds.
- Identifying Algorithmic Decision Subjects' Needs for Meaningful Contestability Peer-reviewed✦ AIEmpirically elicits what decision subjects need for contestation to be 'meaningful', informing the design of effective remedies and appeal mechanisms for ADM.
- Two Means to an End Goal: Connecting Explainability and Contestability in the Regulation of Public Sector AI Preprint✦ AIInterview study with 14 regulation experts distinguishes judicial vs non-judicial and individual vs collective contestation channels for public-sector AI remedies.
- GPTs are GPTs: Labor market impact potential of LLMs Peer-reviewed✦ AIFinds around 80% of the U.S. workforce "could have at least 10% of their work tasks affected" by LLMs, which exhibit "traits of general-purpose technologies".
- Computing Power and the Governance of Artificial Intelligence Preprint✦ AIArgues compute is a uniquely governable lever because it is "detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain".
- Training Compute Thresholds: Features and Functions in AI Regulation Preprint✦ AIFinds "training compute currently is the most suitable metric to identify GPAI models", but thresholds should only trigger further scrutiny, not determine risk measures alone.
- Compute North vs. Compute South: The Uneven Possibilities of Compute-based AI Governance Around the Globe Peer-reviewed✦ AICensus of hyperscale cloud regions shows a divide between "Compute North" states hosting training-relevant compute and a Compute South, shaping who can wield compute-based governance.
- Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity Peer-reviewed✦ AIExamines how the EU AI Act, liability regimes, GDPR, copyright and cybersecurity rules apply to generative AI, identifying gaps and proposing targeted regulatory refinements.
- Evaluating Frontier Models for Dangerous Capabilities Preprint✦ AIPilots dangerous-capability evaluations (persuasion, cyber, self-proliferation) on frontier models, finding 'early warning signs' but no strong present danger — grounding evaluation-based gating.
+ 56 more across this concept's topics — see the literature index.
References
The primary instrument sources behind the article's classifications.
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