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SaferAI is a French nonprofit (association loi 1901) founded in 2023 by Simeon Campos, who previously co-founded EffiSciences, an organization providing training in responsible AI. Headquartered in Paris, SaferAI works across research and policy to advance responsible AI development by modeling complex risks, improving company practices, and contributing to standards and policies.
The organization's work spans three main pillars. First, SaferAI conducts fundamental research developing quantitative risk models that translate AI capabilities into measurable real-world harm assessments. The team has completed nine cyber risk models examining how AI could amplify cyberattack effectiveness, and is expanding into loss of control risks, CBRN threats, and manipulation scenarios. This work draws on established risk management practices from industries like aviation and finance…
SaferAI operates on the theory that establishing rigorous, quantitative risk management infrastructure for AI can make regulation enforceable and incentivize responsible development practices. By creating transparent, independent ratings of AI companies' safety practices, they generate public accountability pressure and provide actionable information to policymakers, investors, and AI users. By contributing to international technical standards and policy frameworks (EU AI Act, ISO, OECD), they help ensure that safety requirements are concrete, measurable, and embedded in enforceable regulation. Their quantitative risk models aim to translate abstract AI capability concerns into specific, measurable harm assessments, bridging the gap between AI safety research and practical risk management that industry and regulators can act on.
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View on grantmaking.aiThe Risk Management Ratings project evaluates the maturity of frontier AI developers’ risk-management practices using SaferAI’s standardized framework. The team reviews companies’ safety frameworks, governance processes, and risk-management documentation and scores them across dimensions such as risk identification, severity assessment, safeguards, and system governance, then publishes comparative ratings and reports. These outputs have generated media coverage and market pressure and are used by institutional investors and other stakeholders as an external benchmark for AI risk management.