UN turns to Google to make its global data ready for AI agents
UN turns to Google to make its global data ready for AI agents
联合国携手谷歌，助力全球数据实现“AI 就绪”
The United Nations on Thursday announced that it is working with Google to make its vast collection of global statistics easier for AI systems to access and use. Called the UN System Data Commons, the new system is built on Google’s open source Data Commons platform and lets people search for statistics from across UN agencies using natural-language queries. 联合国周四宣布，正与谷歌合作，使其庞大的全球统计数据集合更易于人工智能系统访问和使用。这个名为“联合国系统数据共享平台”（UN System Data Commons）的新系统基于谷歌的开源 Data Commons 平台构建，允许用户通过自然语言查询搜索来自各联合国机构的统计数据。
It replaces the existing UNData portal, where users largely had to browse and search for statistics through a more traditional database interface. The new platform also supports the Model Context Protocol (MCP), a standard that allows AI systems to connect directly to external data sources. 该平台取代了现有的 UNData 门户网站，此前用户主要通过传统的数据库界面来浏览和搜索统计数据。新平台还支持模型上下文协议（MCP），这是一种允许 AI 系统直接连接到外部数据源的标准。
Users increasingly turn to AI tools for answers, but many systems still struggle to reliably surface authoritative data. A UNICEF benchmark of six large language models across more than 133,000 responses to questions about global development indicators produced an average accuracy score of just 21.2%, João Pedro Azevedo, the agency’s chief statistician, told reporters in a virtual briefing. 用户越来越倾向于使用 AI 工具获取答案，但许多系统在可靠地呈现权威数据方面仍面临挑战。联合国儿童基金会（UNICEF）首席统计学家若昂·佩德罗·阿泽维多（João Pedro Azevedo）在一次虚拟简报会上告诉记者，他们对六种大语言模型进行了基准测试，涵盖了超过 13.3 万条关于全球发展指标的问题回答，结果显示平均准确率仅为 21.2%。
The test covered OpenAI’s GPT-4o and GPT-4o-mini, Anthropic’s Claude Sonnet 4.5 and Haiku 4.5, and Google’s Gemini 2.5 Flash and Gemini 2.0 Flash, Azevedo told TechCrunch. About three in five responses did not provide a usable number at all, often because the models hedged their answers, Azevedo said. 阿泽维多向 TechCrunch 透露，此次测试涵盖了 OpenAI 的 GPT-4o 和 GPT-4o-mini、Anthropic 的 Claude Sonnet 4.5 和 Haiku 4.5，以及谷歌的 Gemini 2.5 Flash 和 Gemini 2.0 Flash。阿泽维多表示，约五分之三的回答根本没有提供可用的数字，这通常是因为模型在回答时采取了模棱两可的态度。
However, when the same questions were run again on the same model versions about two days later, models that provided a number both times returned the identical number only about half the time. The study is a UNICEF working paper being prepared for journal submission and has not yet been peer-reviewed. The organization said it plans to release its methodology, code, and data alongside the paper. 然而，当大约两天后在相同模型版本上再次运行相同问题时，两次都提供了数字的模型中，只有约一半的情况返回了相同的数字。这项研究是一份正在准备提交期刊的 UNICEF 工作论文，尚未经过同行评审。该组织表示，计划在发布论文的同时公布其方法、代码和数据。
UNICEF has also seen a sharp rise this year in traffic from generative AI assistants to its data website, which receives more than 6 million visits a month and is among the agency’s most popular websites. Visits from users clicking links in ChatGPT answers to the site rose 67% year-over-year between January 1 and September 14, Azevedo told TechCrunch. Such referrals accounted for 6.4% of all sessions this year, while UNICEF estimates that AI assistants overall now account for about one in 10 visits. 今年，UNICEF 还观察到来自生成式 AI 助手的流量大幅增长。其数据网站每月访问量超过 600 万次，是该机构最受欢迎的网站之一。阿泽维多告诉 TechCrunch，从 1 月 1 日到 9 月 14 日，用户点击 ChatGPT 回答中的链接访问该网站的次数同比增长了 67%。此类引流占今年所有会话的 6.4%，而 UNICEF 估计，目前 AI 助手带来的访问量约占总访问量的十分之一。
The UN said 26 of its entities have committed to the Data Commons, with data from nearly 20 available at launch. Moreover, it aims to bring 80% of the UN system’s statistical datasets onto the platform by 2027. 联合国表示，已有 26 个实体承诺加入 Data Commons，其中近 20 个实体的数据在发布时即可使用。此外，其目标是在 2027 年之前将联合国系统 80% 的统计数据集整合到该平台上。
“We are orders of magnitude more advanced in scale, scope, and flexibility, connecting for the first time across so many agencies across the UN system,” said Shantanu Mukherjee, acting director of the UN Statistics Division. “And [we are] taking this moment to also make our data AI-ready.” “我们在规模、范围和灵活性方面有了数量级的提升，首次实现了联合国系统内众多机构的互联互通，”联合国统计司代理司长尚塔努·穆克吉（Shantanu Mukherjee）表示，“我们同时也借此机会让我们的数据实现‘AI 就绪’。”
Google.org provided $2 million in capacity-building funding and technical support to establish the platform’s core infrastructure. Prem Ramaswami, who leads Google’s Data Commons team, told TechCrunch that the system is hosted on a UN-governed instance and is intended to eventually be maintained, operated, and scaled independently by the UN. Google.org 提供了 200 万美元的能力建设资金和技术支持，用于建立平台的核心基础设施。谷歌 Data Commons 团队负责人普雷姆·拉马斯瓦米（Prem Ramaswami）告诉 TechCrunch，该系统托管在由联合国管理的实例上，旨在最终由联合国独立维护、运营和扩展。
“We have taken a “train-the-trainer” approach throughout the rollout, and we have already seen the UN system team ramp up quickly,” Ramaswami said. “我们在整个推广过程中采取了‘培训培训师’的方法，我们已经看到联合国系统团队迅速上手，”拉马斯瓦米说。
Google launched Data Commons in 2018 as an effort to organize public datasets from different sources into a common framework. Last year, it added support for MCP, allowing AI agents to directly query Data Commons for statistics and their sources. The UN’s platform also keeps track of where each statistic comes from, so people can trace data retrieved by an AI system back to the original UN source. 谷歌于 2018 年推出了 Data Commons，旨在将来自不同来源的公共数据集组织到一个通用框架中。去年，它增加了对 MCP 的支持，允许 AI 代理直接向 Data Commons 查询统计数据及其来源。联合国的平台还会追踪每项统计数据的来源，以便人们能够将 AI 系统检索到的数据追溯到原始的联合国出处。
Azevedo told reporters that it was important as more people rely on AI tools to find and interpret information. Alongside enabling AI agents to retrieve individual statistics, Google demonstrated how an AI system connected to the UN data through MCP could pull together multiple indicators and use them to generate dashboards, charts, and written analysis without a user having to manually find and combine the underlying datasets. 阿泽维多告诉记者，随着越来越多的人依赖 AI 工具来查找和解读信息，这一点至关重要。除了使 AI 代理能够检索单个统计数据外，谷歌还演示了通过 MCP 连接到联合国数据的 AI 系统如何整合多个指标，并利用它们生成仪表板、图表和书面分析，而无需用户手动查找和合并底层数据集。
In one demonstration, Google asked an AI system to find the impact of the U.S. President’s Emergency Plan for AIDS Relief in Africa. The system identified relevant UN statistics on measures such as HIV infections, AIDS mortality, and life expectancy, and used them to produce an infographic. 在一次演示中，谷歌要求 AI 系统查找美国总统艾滋病紧急救援计划（PEPFAR）在非洲的影响。该系统识别了有关艾滋病毒感染、艾滋病死亡率和预期寿命等指标的联合国相关统计数据，并利用这些数据制作了一张信息图。
However, giving an AI system authoritative data does not necessarily make its conclusions authoritative. “Because models can misinterpret nuance, a human should always review the outputs before citing or publishing them,” Ramaswami said. 然而，为 AI 系统提供权威数据并不一定使其结论也具有权威性。“由于模型可能会误解细微差别，人类在引用或发布输出结果之前，应始终进行审核，”拉马斯瓦米说。