The 2026 Singapore Consensus on Global AI Safety Research Priorities

Applies To

Overview

The 2026 Singapore Consensus is the second edition of a research priorities document identifying the most pressing open problems in AI safety. It followed the second International Scientific Exchange on AI Safety, which IMDA hosted in Singapore on 18 and 19 May 2026. More than 100 contributors from 13 countries wrote it, from frontier AI developers, government safety institutes, academia, and civil society. It covers model evaluation, robustness (reliability under unexpected inputs), interpretability (seeing why a model acts as it does), and alignment, plus governance topics like incident reporting and auditing standards. The document is not a binding regulation but functions as an authoritative reference that informs enterprise safety programs, procurement standards, and regulatory submissions. It builds on the 2025 edition by updating priority rankings based on advances in the most capable AI models and published safety research. While enforcement is absent in the traditional regulatory sense, its adoption by major research institutions and fit with emerging regulatory expectations gives it practical weight in compliance contexts. Organizations subject to the EU AI Act, UK AI Safety Institute guidance, or Singapore Infocomm Media Development Authority (IMDA) frameworks may find following it helps show good-faith safety diligence.

Key Requirements

• •No legally binding obligations; the document establishes research priorities rather than enforceable mandates.
• •Recommends structured pre-deployment evaluation covering robustness, alignment, and capability thresholds before models are released or integrated.
• •Calls for standardized documentation practices for model behavior, training data provenance, and known failure modes.
• •Advocates for incident reporting mechanisms that feed back into iterative safety research cycles.
• •Emphasizes governance structures including internal oversight roles and third-party auditing for high-capability systems.
• •Highlights alignment with international frameworks including OECD AI Principles and the EU AI Act risk classification as reference points for enterprises.

What Your Organization Must Do

• →Map your model evaluation practices against the Consensus priorities on robustness, alignment, and interpretability.
• →Treat the document as voluntary guidance, not a legal duty, in internal policy records.
• →Document model behavior, training data sources, and known failure modes in a consistent format.
• →Set up an internal incident reporting route that feeds findings back into safety testing.
• →Assign internal oversight roles and consider third-party audits for your most capable systems.
• →Reference the Consensus in regulatory submissions to show good-faith safety diligence where relevant.