顶级顶尖科学家首次齐聚新加坡，共同推进值得信赖、可靠且安全的人工智能。
“新加坡人工智能会议：国际人工智能科学交流” （SCAI：ISE）首次在新加坡举办，为2025年4月26日开幕的新加坡人工智能探索周增添了新气息。来自11个国家（澳大利亚、加拿大、智利、中国、法国、日本、韩国、荷兰、英国、美国和新加坡）的人工智能安全研究领域的杰出人士，包括学者、行业参与者、政府代表、智库和政策制定者，齐聚一堂，共同探讨人工智能安全研究的重要性以及达成国际共识的必要性，并最终发布了《新加坡全球人工智能安全研究重点共识》（简称《新加坡共识》）。
新加坡始终以未雨绸缪的态度拥抱新技术。在人工智能领域，则始终采取全球合作方式探索如何使安全可靠值得信赖的人工智能。随着人工智能模型的进步及其在不同领域的广泛应用，开发通用的评估基准、方法和工具以管理此类模型可能带来的固有风险的需求日益增加。SCAI:ISE 是实现这一目标的最新里程碑，它召集了来自 11 个国家的 100 多位人工智能安全研究领域的参与者（例如 Yoshua Bengio 和 Max Tegmark），共同确定需要研究的内容、确定研究的优先顺序并达成共识。
SCAI:ISE，“新加坡共识”确定了以下三大人工智能安全研究重点领域:
风险评估：风险评估的主要目标是了解潜在危害的严重性和可能性，这有助于确定风险的优先级并确定是否需要采取行动。此类别的研究领域包括开发衡量人工智能系统对当前和未来人工智能影响的方法、增强计量技术以确保这些测量的精确性和可重复性，构建并推动第三方审计以支持对这些风险评估的独立验证。
开发：遵循经典的安全工程框架，设计安全可靠值得信赖的人工智能系统，才能让人们充满信心地拥抱和采用人工智能创新。此类研究领域涵盖定义期望行为、设计符合规范的系统以及验证人工管控：在工程学中，“管控”通常指通过管理系统来实现目标，即使在面临干扰或不确定性的情况下，也能进行日常的循环反馈。此类研究领域涵盖开发人工智能系统的监控和干预机制，将监控机制扩展到人工智能系统所属的更广泛的人工智能生态系统，以及开展社会韧性研究，以加强社会基础设施（例如经济、安全）抵御人工智能带来的破坏和滥用。
新加坡致力于以科学为导向以实践循证为依据的人工智能治理方法，不仅要有充足的护栏保护人民，还要为创新提供最大的空间。这对于构建安全可靠值得信赖的人工智能生态系统至为重要。新加坡数码发展及新闻部长杨莉明在开幕式致辞中表示：在研究界和政界之间建立更紧密的联系至关重要，这样才能将人工智能安全研究转化为切实有效的政策，从而管理好人工只能。此次科学交流为AI科学家和AI决策者搭建对话的桥梁，并以此作为持续对话的一部分，帮助各国政府在科学证据的基础上，在安全与创新之间取得微妙的平衡。
为了影响政策的制定，“新加坡共识” 将在5月28日至29日举行的亚洲科技与新加坡峰会（ATxSummit）上向出席部长圆桌会议的数字部长们进行宣读。最终目标是建立信任的良性循环，更重要的是要确保人工只能能够造福公众。
照片来源：MDDI ，首页照片：Minister of Digital Development and Information, Mrs Josephine Teo.
（戴凯老师 报道）
Top scientific minds gathered for the first time in Singapore to advance AI that is trustworthy, reliable and secure
SINGAPORE, 8 May 2025 — As part of Singapore AI Research Week, the “Singapore Conference on AI: International Scientific Exchange on AI Safety” (SCAI: ISE) took place on 26th April 2025, organised on the sidelines of the International Conference on Learning Representations (ICLR) 2025 held in Singapore this year for the first time. Luminaries in the field of AI safety research from 11 countries (Australia, Canada, Chile, China, France, Japan, Korea, Netherlands, UK, US and Singapore), including academics, industry players, government representatives, think tanks and policy makers, gathered for the conference to discuss the importance of AI safety research and the need for an international consensus, resulting in the publication of “The Singapore Consensus on Global AI Safety Research Priorities” (The Singapore Consensus).
2. Singapore has always embraced new technologies by being prepared. In the area of AI, Singapore has consistently taken a globally collaborative approach in understanding how to make AI trustworthy, reliable and secure. With the advancement of AI models and its increasing use in different sectors, there is a greater need to develop common evaluation benchmarks, methodologies and tools to manage the inherent risks that such models may bring. The SCAI:ISE is the latest milestone step to do just that by convening over 100 participants (such as Yoshua Bengio and Max Tegmark) from 11 countries in the field of AI safety research to identify, prioritise and arrive at a consensus on what needs to be researched on. Through SCAI:ISE, the “The Singapore Consensus” identified the following 3 broad areas of AI safety research priorities.
• Risk Assessment: The primary goal of risk assessment is to understand the severity and likelihood of a potential harm, which can then help priotiise risks and determine whether action needs to be taken. The research areas in this category involve developing methods to
measure the impact of AI systems for both current and future AI, enhancing metrology to
ensure that these measurements are precise and repeatable, and building enablers for third-party audits to support independent validation of these risk assessments.
• Development: AI systems that are trustworthy, reliable and secure by design give people the confidence to embrace and adopt AI innovation. Following a classic safety engineering framework, the research areas in this category involves specifying the desired behaviour, designing a system that meets the specification, and verifying that the AI system meet its specification.
• Control: In engineering, “control” usually refers to the process of managing a system’s behaviour to achieve a desired outcome, even when faced with disturbances or uncertainties, and often in a feedback loop. The research areas in this category involve developing monitoring and intervention mechanisms for AI systems, extending monitoring mechanisms to the broader AI ecosystem to which the AI system belongs, and societal resilience research to strengthen societal infrastructure (e,g, economic, security) against AI-enabled disruption and misuse.
3. Singapore remains committed to a scientifically driven and evidence-based approach to AI governance. This is essential to building a trustworthy, reliable and secure AI ecosystem where there are sufficient guardrails to protect people, while providing maximal space for innovation. In her opening remarks, Minister for Digital Development and Information, Mrs Josephine Teo, shared that it is important to build stronger pathways between the research world and policy making world to translate AI safety research into real effective policies to govern AI well. Through this scientific exchange, “The Singapore Consensus” aims to bridge discussions between AI scientists and AI policy makers, as part of a continuous dialogue to help governments strike the delicate balance of safety and innovation based on scientific evidence. To influence policy-making, the “The Singapore Consensus” will be presented to digital ministers attending the ministerial roundtable at the upcoming Asia Tech x Singapore Summit (ATxSummit) from 28-29 May 2025. The end-goal is to create a virtuous cycle of trust and, more importantly, ensure that AI is harnessed for the public good.
4. Internationally, this scientific exchange is the latest in a series of milestone steps in building a trusted AI ecosystem. The AI Verify Foundation (AIVF) previously launched two toolkits, the AI Verify Testing Toolkit and Project Moonshot, to test traditional and Gen AI models respectively to help developers, compliance teams, and AI system owners manage deployment risks. Singapore’s IMDA along with AIVF also released the Model AI Governance Framework for Gen AI in 2024 that provided a systematic and balanced approach to address Gen AI concerns while facilitating innovation. In November and December 2024, IMDA, in partnership with Humane Intelligence, conducted the world’s first multicultural and multilingual red-teaming exercise and published the “Singapore AI Safety Red-Teaming Challenge Evaluation Report” on it. AIVF also launched the “Global AI Assurance Pilot” earlier this year to help codify emerging norms and best practices around technical testing of Gen AI applications.
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