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This paper explores how machine learning can help classify aid activities by sector using the OECD Creditor Reporting System (CRS). The CRS is a key source of data for monitoring and evaluating aid flows in line with the United Nations Sustainable Development Goals (SDGs), especially SDG17 which calls for global partnership and data sharing. To address the challenges of current labor-intensive practices of assigning the code and the related human inefficiencies, we propose a machine learning solution that uses ELECTRA to suggest relevant five-digit purpose codes in CRS for aid activities, achieving an accuracy of 0.9575 for the top-3 recommendations. We also conduct qualitative research based on semi-structured interviews and focus group discussions with SDG experts who assess the model results and provide feedback. We discuss the policy, practical, and methodological implications of our work and highlight the potential of AI applications to improve routine tasks in the public sector and foster partnerships for achieving the SDGs. ■ DOI: https://doi.org/10.24963/ijcai.2023/670

International aid is a critical mechanism for promoting economic growth and well-being in developing nations, supporting progress toward the Sustainable Development Goals (SDGs). However, tracking aid contributions remains challenging due to labor-intensive data management, incomplete records, and the heterogeneous nature of aid data. Recognizing the urgency of this challenge, we partnered with government agencies to develop an AI model that complements manual classification and mitigates human bias in subjective interpretation. By integrating SDG-specific semantics and leveraging prior knowledge from language models, our approach enhances classification accuracy and accommodates the diversity of aid projects. When applied to a comprehensive dataset spanning multiple years, our model can reveal hidden trends in the temporal evolution of international development cooperation. Expert interviews further suggest how these insights can empower policymakers with data-driven decision-making tools, ultimately improving aid effectiveness and supporting progress toward SDGs. ■ DOI: https://doi.org/10.48550/arXiv.2505.15223

This study examines the evolution of transnational aid data governance through an in-depth analysis of the OECD Creditor Reporting System and the International Aid Transparency Initiative. Conceptualizing data governance as a socio-technical and politically contested process, it explores how the norms of aid transparency and aid effectiveness have diffused globally, and how reporting standards have emerged and become institutionalized within the fragmented architecture of international development cooperation. The study highlights how regime complexity, characterized by overlapping mandates, institutional tensions, and competing mechanisms, has shaped the trajectory of aid data governance. The findings demonstrate that aid data governance is driven not only by technical rationales and functional imperatives but also by political interests and institutional dynamics. Drawing on qualitative case analysis, the study identifies persistent challenges in aligning transparency norms with reporting practices. It calls for a multidisciplinary approach to future research and for adaptive, interoperable frameworks tailored to post-2030 development agendas. ■ DOI: https://doi.org/10.17645/pag.10508