Navigating Alignment in AI Systems as DPGs

Navigating Alignment in AI Systems as DPGsDuring this year’s UN Open Source Week in New York, many discussions will address AI as both a significant opportunity and a challenge for digital public goods (DPGs) and the wider open-source ecosystem. Simultaneously, the debate of the characteristics of AI systems as DPGs continues, transcending the DPG Standard while rooted in OSI’s definition of open-source AI. The G7 also recently published its vision on openness in AI systems, including a description of elements that determine the different levels of openness. The fact is that today there's no global consensus on what openness in AI systems actually means.At the DPGA Secretariat, we are focused on operationalising the commitments of the Global Digital Compact (GDC) to “develop, disseminate and maintain, through multi-stakeholder cooperation, [...] open data, [and] open artificial intelligence models” to help achieve the SDGs by 2030. However, translating these high-level ambitions into operational practices is part of a complex implementation process. Welcoming this ongoing debate as a vital step forward, we turn our attention to the new report commissioned by the DPGA member, the Asian Development Bank: “AI Systems as Digital Public Goods: Evidence and Recommendations from a Multi-Stakeholder Assessment”. The report was produced by the United Nations University Macau in partnership with the UN Office for Digital and Emerging Technologies (UN ODET), and arrives at a critical time. It provides valuable input into our ongoing standard-setting process as we review how the open data clause of the DPG Standard applies to AI systems one year after its last update. Strategic Alignment with the DPG Standard and the DPGA Secretariat’s WorkWhen the DPGA Secretariat updated the DPG Standard for AI systems in 2025—following extensive consultations within our AI Community of Practice co-hosted with UNICEF—we deliberately set a high, aspirational bar. We wanted to move the conversation away from treating AI as an isolated technology, toward a holistic view of AI as a socio-technical system.The now-published “AI Systems as Digital Public Goods” report organises its recommendations around the SAFE mnemonic — Standard, Accountability, Finance, Equity — and is candid that several of its recommendations fall beyond the DPGA Secretariat's current mandate. These are still valuable precisely because they are ecosystem-level responsibilities, shared across multilateral development banks, donors, governments, and standard-setters alike. Here is how those recommendations map against the DPG Standard as it stands today, and where we either already meet the suggested requirements or see things differently.

AI systems as digital public goods - a dive into what this means from a slightly more technical perspective

The DPGA is now accepting submissions for AI systems! This post provides a practical overview and detailed description of the requirements outlined in the DPG Standard that an AI system must meet to be recognized as a digital public good and listed on the DPG Registry.How we got here ?A digital public good (DPG) entails much more than simply being open software, open data, an open content collection, or an open AI system. DPGs are open-source solutions that must also be accessible, adaptable, and designed to do no harm. Therefore, to be recognised as a DPG, a solution must demonstrate adherence to the DPG Standard to ensure those important elements are embedded into the design of a digital solution and, by doing so, can facilitate more impactful and safe technology deployment. In 2023, recognising both the immense potential of AI for development as well as the risks associated with it, the DPGA Secretariat, in collaboration with UNICEF, convened a dedicated Community of Practice (CoP) on AI systems. This group was brought together to specifically examine how the DPG Standard may need to adapt to better determine what constitutes AI systems as a type of DPG and to explore the intersection between open and responsible AI. Alongside a set of recommendations from the CoP on AI systems, the DPGA Secretariat closely monitored and participated in relevant conversations that were also taking place. This included the OSI's Open Source AI Definition (OSAID), the Linux Foundation’s Model Openness Framework (MOF), and consultations with DPGA members actively working on AI, such as OpenFuture, Creative Commons, and the Open Knowledge Foundation.Following an open commenting period on GitHub, the DPG Standard Council carefully considered inputs surfaced throughout this process and, as part of a set of updates to the DPG Standard, introduced changes to strengthen the transparency and accountability of AI system DPGs while ensuring that they meet consistent requirements across all DPG categories.What's an AI system, anyway?Before diving into the specific updates, it’s valuable to provide an understanding of what is meant by “AI system,” as it has implications for the components that must be DPG Standard compliant. We recognise AI systems as machine-based systems designed to operate with varying levels of autonomy that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments, or generate outputs, such as text, images, or sounds.This understanding aligns with OECD guidelines on AI (Recommendation of the Council on Artificial Intelligence, OECD Publishing, Paris, 2019)In order to be recognised as a DPG status, AI systems must provide the following components, alongside the following requirements: