Article by Haishan Fu, Aivin Solatorio, Olivier Dupriez and Craig Hammer: “AI, particularly large language models (LLMs), is completely transforming the way people interact with data. Data users at all levels of experience and expertise—from first-timers to power users—are now able to pose complex questions in natural language to chatbots, to which they expect to promptly find, interpret, and present data-driven insights packaged as pithy, accurate responses.
For this evolution to be successful, AI systems need to get it right. This means the data being accessed and interpreted by AI systems must first be evaluated, validated, structured, governed, and shared in ways that support the responsible and effective use of AI. In short, the data must be “AI-ready.”
AI-ready data does not supplant earlier advancements, foundational concepts, or standards—such as the Fundamental Principles of Official Statistics, open data frameworks, or the FAIR (Findable, Accessible, Interoperable, and Reusable) principles—but rather it builds on them. By extending established foundations and standards, AI-ready data means that development data is continuously open, discoverable, and reusable, while ensuring that it is systematically organized and well-documented, to facilitate seamless use by both people and AI systems. Ensuring AI-readiness can thus shorten the distance between development data and decision-making for better policies and faster innovation, democratizing development insights. The World Bank, in its efforts to become a bigger, better “Data Bank,” is already working to make this happen, in partnership with country partners and the global development community…(More)” See also: Moving Toward the FAIR-R principles: Advancing AI-Ready Data.
