A global coalition of AI researchers, policymakers, and technologists has released the Singapore Consensus, a landmark international roadmap outlining research priorities for technical AI safety. Unveiled at the Singapore Conference on AI (SCAI), the document represents one of the most ambitious attempts yet to coordinate international efforts toward ensuring trustworthy, reliable, and secure artificial intelligence systems.
The Consensus, developed by more than 100 contributors from 11 countries, builds upon the 2025 International AI Safety Report and incorporates input from leading organizations such as MILA, UC Berkeley, DeepMind, and the UK’s AISI. It organizes research priorities across three interlocking domains: risk assessment, development of trustworthy systems, and post-deployment control.
In the risk assessment category, the report stresses the need for robust benchmarks, third-party audits, and secure evaluation infrastructure. Researchers are called to create methods that can detect dangerous capabilities, assess downstream impacts, and identify potential loss-of-control risks in general-purpose AI systems. The authors highlight the difficulty of forecasting societal impacts and the importance of “prospective risk analysis,” comparing it to methods used in aviation and nuclear safety.
The second section addresses the development of secure and trustworthy AI systems by emphasizing the need for precise specification and validation of desired behaviors. It calls for new approaches to training data curation, robustness testing, and model editing to ensure systems not only behave as intended but also resist tampering, jailbreaks, and misuse. The document introduces a framework that categorizes AGI as the convergence of autonomy, generality, and intelligence—and suggests mitigating risk by deliberately limiting one or more of those attributes in system design.
Post-deployment monitoring and control is the final pillar of the Consensus. The report calls for continuous oversight, including ecosystem-wide monitoring and societal resilience research. It advocates for feedback-loop interventions and stresses the need for scalable interpretability and red-teaming methods that can reveal failures in real-world environments, especially in multi-agent contexts.
Unlike previous safety reports, the Singapore Consensus explicitly identifies areas of “mutual interest” where stakeholders—including rival companies and nations—may benefit from cooperation. These include the development of shared risk thresholds, evaluation protocols, and safety benchmarks. The authors draw comparisons to aviation safety, where even fierce competitors collaborate to prevent accidents that could damage the entire industry.
With rapid advancements in general-purpose AI and increasing geopolitical tensions over its regulation, the Singapore Consensus aims to foster a collaborative, science-driven approach to AI safety. As Max Tegmark of MIT and Yoshua Bengio of MILA—two of the report’s key architects—have emphasized in past work, the long-term success of AI depends not just on breakthroughs in capability, but also on a shared global commitment to making AI safe by design.
Need Help?
If you’re concerned or have questions about how to navigate Singapore’s, or any country’s, AI regulatory landscape, don’t hesitate to reach out to BABL AI. Their Audit Experts can offer valuable insight and ensure you’re informed and compliant.
