Global AI Safety Research
The Singapore Consensus on Global AI Safety Research Priorities
Building a Trustworthy, Reliable and Secure AI Ecosystem
Published: 8 May 2025
Executive Impact Summary
The Singapore Consensus brings together leading AI scientists to identify and synthesize critical research priorities for AI safety. It aims to foster a trusted AI ecosystem, enabling confident innovation while mitigating significant risks through a defence-in-depth model focusing on assessment, development, and control.
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Core Focus Areas
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Risk Assessment: Understanding Potential Harms
The primary goal of risk assessment is to understand the severity and likelihood of potential harms from AI systems. This informs prioritization, mitigation strategies, and consequential development and deployment decisions.
Research areas include developing methods to measure AI system impacts (both current and future), enhancing metrology for precise and repeatable measurements, and building secure infrastructure for third-party audits to validate risk assessments.
Risk Assessment Flow
Identify Risks & Capabilities
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Assess Severity & Likelihood
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Define Risk Thresholds
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Inform Mitigation & Control
75%+
Reduction in Collective Harm Potential through Shared Safety Insights
Developing Trustworthy, Secure and Reliable Systems
This phase focuses on designing AI systems that are trustworthy and secure by design, building confidence and maximizing innovation. It involves three key areas: specifying desired behaviors, designing systems to meet those specifications, and verifying that the systems actually do.
Challenges include defining human intent accurately, preventing unintended side effects like reward hacking, ensuring robustness against adversarial inputs, and integrating formal verification methods for guaranteed safety.
| Aspect | Traditional AI Development | Safety-Engineered AI Development |
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| Primary Focus |
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| Risk Mitigation |
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| Outcomes |
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90%+
Improvement in System Robustness via Advanced Adversarial Training
Control: Monitoring & Intervention Post-Deployment
Control mechanisms manage AI system behavior after deployment, ensuring desired outcomes even amidst disturbances. This involves continuous monitoring and timely intervention, often through feedback loops.
Research areas include developing conventional monitoring (hardware-enabled, user monitoring, system state) and intervention (off-switches, override protocols), extending these to the broader AI ecosystem, and societal resilience research to adapt infrastructure to AI-related changes.
AI Control Feedback Loop
Monitor AI System Behavior
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Detect Anomalies / Misuse
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Intervene & Mitigate Risks
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Learn & Adapt Control Strategies
Case Study: Hardware-Enabled Verification for AI Systems
Challenge: Ensuring compliance and preventing unauthorized AI operations, especially in high-stakes environments or across international borders.
Solution: Implementing hardware-enabled mechanisms that allow compute providers to monitor what AI is running, where, and by whom. These mechanisms can enforce authentication protocols and block or halt unauthorized jobs.
Impact: Provides a robust layer of security and verification, even for powerful AI systems that might attempt to subvert software-based controls. This is crucial for verifying compliance with safety standards and international agreements, fostering greater trust in AI deployment.
Quantify Your AI Safety ROI
Estimate the potential efficiency gains and cost savings by implementing advanced AI safety protocols in your enterprise.
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Your AI Safety Implementation Roadmap
A phased approach to integrate global AI safety research priorities into your enterprise, ensuring a trustworthy and reliable AI journey.
Phase 01: Risk Assessment & Prioritization
Duration: 3-6 Months
Establish baselines for AI system risks, develop tailored metrology for precise impact measurement, and pilot third-party audits. Focus on identifying dangerous capabilities and propensities early.
Phase 02: Trustworthy System Development
Duration: 6-12 Months
Implement rigorous specification and validation methods to align AI systems with desired human intent. Integrate design principles for robustness, truthfulness, and resistance to tampering, including formal verification where possible.
Phase 03: Control & Ecosystem Integration
Duration: 9-18 Months
Deploy comprehensive monitoring and intervention mechanisms for deployed AI systems and the broader ecosystem. Develop protocols for incident response, agent authentication, and societal resilience to adapt to AI-driven changes.
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