AI Systems as Digital Public Goods -- Evidence and Recommendations from a Multi-Stakeholder Assessment
2026-07-03 • Computers and Society
Computers and Society
AI summaryⓘ
The authors looked into why few AI systems currently qualify as Digital Public Goods (DPGs), despite global support for open and secure AI to help achieve Sustainable Development Goals. They explain what changes are needed to make AI truly useful and accessible as DPGs, instead of just being a hopeful idea. Their study, done for the Asian Development Bank and involving the United Nations, used document reviews, expert interviews, and a worldwide survey to understand challenges and differences across regions and sectors.
Digital Public GoodsSustainable Development GoalsArtificial IntelligenceOpen Source SoftwareAI GovernanceGlobal Digital CompactPolicy FrameworksAI ReadinessUnited NationsAsian Development Bank
Authors
Serge Stinckwich, Natalie Wong, Ally S. Nyamawe, Jia'An Liu, Farhan Latif, Jaimee Stuart
Abstract
AI systems are increasingly being positioned as potential Digital Public Goods (DPGs) to accelerate progress towards the Sustainable Development Goals (SDGs). Yet, despite major global commitments, most notably the Global Digital Compact's call to "develop, disseminate and maintain safe and secure open-source software, open data, open artificial intelligence models and open standards that benefit society as a whole", very few AI systems currently meet the DPG Standard in practice. This report explains why, and what must change for "AI as Digital Public Goods" (AIDPGs) to become a credible, implementable pathway rather than an aspirational label. Commissioned by the Asian Development Bank (ADB) and produced by United Nations University (UNU) in partnership with UN Office of Digital and Emergent Technologies (UN ODET), this assessment combines: (i) a structured desk review of policy, legal, and technical frameworks on DPGs, openness, and AI governance; (ii) key informant interviews with cross sector experts spanning the UN system, governments, civil society, academia, and the private sector; and (iii) a global survey to test whether interview themes hold across a broader sample and to surface where perspectives diverge by region, sector, and AI readiness.