Research

Our research

Our research explores the technical, political, and institutional challenges of building safer AI systems. We aim to inform policy, support practitioners, and contribute to a growing body of knowledge that bridges theory and real-world impact.

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  • 24.03.2026
  • Memo
The Case for European Investment in High-Risk, High-Reward AI Reliability Research
Chloé Touzet, Lily Stelling, Bruno Galizzi
This paper outlines the economic and strategic rationale for European investment, diagnoses the market failure that justifies public intervention, identifies the institutional design features that would maximise the likelihood of success, and explains why existing research and innovation instruments...
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  • 10.12.2025
  • Technical Report
Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse
Steve Barrett, Malcolm Murray, Otter Quarks, Matthew Smith, Jakub Kryś, Siméon Campos, Alejandro Tlaie Boria, Chloé Touzet, Sevan Hayrapet, Fred Heiding, Omer Nevo, Adam Swanda, Jair Aguirre, Asher Brass Gershovich, Eric Clay, Ryan Fetterman, Mario Fritz, Marc Juarez, Vasilios Mavroudis, Henry Papadatos
Advanced AI systems offer substantial benefits but also introduce risks. In 2025, AI-enabled cyber offense has emerged as a concrete example. This technical report applies a quantitative risk modeling methodology (described in full in a companion paper) to this domain. We develop nine detailed cyber...
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  • 10.12.2025
  • Paper
A Methodology for Quantitative AI Risk Modeling
Malcolm Murray, Steve Barrett, Henry Papadatos, Otter Quarks, Matt Smith, Alejandro Tlaie Boria, Chloé Touzet, Siméon Campos
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  • 10.12.2025
  • Paper
The Role of Risk Modeling in Advanced AI Risk Management
Chloé Touzet, Henry Papadatos Malcolm Murray, Otter Quarks, Steve Barrett, Alejandro Tlaie Boria, Elija Perrier, Matthew Smith, Siméon Campos
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  • 03.11.2025
  • Memo
Safety Frameworks and Standards: A comparative analysis to advance risk management of frontier AI
James Gealy, Daniel Kossack, Simeon Campos
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