UN Partners with Google to Make Global Data AI-Ready

The United Nations announced Thursday it is working with Google to make its vast collection of global statistics easier for AI systems to access and use.

The new system, called the UN System Data Commons, is built on Google's open source Data Commons platform. It allows users to search for statistics from across UN agencies using natural-language queries, replacing the existing UNData portal where users had to browse through a traditional database interface. The platform also supports the Model Context Protocol (MCP), a standard that lets AI systems connect directly to external data sources.

The move comes as AI tools 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%, according to João Pedro Azevedo, the agency's chief statistician. About three in five responses did not provide a usable number at all. The study is a UNICEF working paper preparing for journal submission and has not yet been peer-reviewed.

UNICEF has also seen a sharp rise in traffic from generative AI assistants to its data website, which receives more than 6 million visits a month. Visits from users clicking links in ChatGPT answers rose 67% year-over-year between January 1 and September 14, Azevedo said.

The UN said 26 of its entities have committed to the Data Commons, with data from nearly 20 available at launch. It aims to bring 80% of the UN system's statistical datasets onto the platform by 2027.

Google.org provided $2 million in capacity-building funding and technical support. Prem Ramaswami, who leads Google's Data Commons team, said the system is hosted on a UN-governed instance and is intended to eventually be maintained and scaled independently by the UN.

The platform tracks where each statistic comes from, allowing people to trace data retrieved by an AI system back to the original UN source. However, Ramaswami cautioned that 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," he said.

Source: TechCrunch