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Classifying and Tracking International Aid Contribution Towards SDGs

Proceedings of the 34th International Joint
Conference on Artificial Intelligence (IJCAI-25)

Proceedings of the 34th
International Joint Conference on Artificial Intelligence (IJCAI-25)

Classifying and Tracking
International Aid Contribution
Towards SDGs

Sungwon Park, Dongjoon Lee, Kyeongjin Ahn, Yubin Choi, Junho Lee,
Meeyoung Cha and Kyung Ryul Park

AI-Powered Global Aid Intelligence Platform
Mapping, Tracking, and Analyzing Development Aid

What is ODA & CRS?

Official Development Assistance (ODA) refers to government-to-government aid flows aimed at supporting economic development and
improving welfare in low-income countries. It plays a crucial role in the poorest and most vulnerable countries with limited resources.

The most authoritative database for monitoring and evaluating ODA flows is the OECD's Creditor Reporting System (CRS).
Donor governments are required to report ODA project information to the CRS annually and systematically classify it according to
internationally agreed coding standards.

  • Official
    Development
    Assistance
  • SDGs
  • Creditor
    Reporting
    System

Research Motivation

  • In 2018, the OECD introduced the SDG Focus Field to strengthen the link between Official Development Assistance (ODA) activities and the Sustainable Development Goals (SDGs).

    This field was added to the Creditor Reporting System (CRS) to allow member countries to clearly indicate how their ODA projects contribute to specific SDG goals and targets. Currently, reporting on the SDG Focus Field remains voluntary for member countries.

  • The SDGs consist of 17 Goals and 169 Targets, making the manual review and classification of each ODA project a highly complex and time-consuming process. Such manual classification increases human and financial costs and leads to inconsistencies in SDG reporting across countries and donor agencies.

  • To address these challenges, the introduction of an Automated SDG Classification Model is essential. Automation can enhance the efficiency of project document analysis, reduce workload and operational costs, and establish a standardized linkage between ODA projects and SDG goals.

    Moreover, such a system can provide a new operational standard for integrating the SDGs throughout the ODA project cycle and enable data-driven decision-making for donor institutions and policymakers—ultimately contributing to greater effectiveness, transparency, and accountability in global development cooperation.

Research Objective

Classification Model

research * is to develop an AI-based model that automatically classifies Official Development Assistance (ODA) projects according to the Sustainable Development Goals (SDGs).

By leveraging artificial intelligence, the model will enhance efficiency, consistency, and transparency in SDG reporting, providing a robust foundation for data-driven analysis and decision-making in development cooperation.

* Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI-25) "Classifying and Tracking International Aid Contribution Towards SDG" Sungwon Park, Dongjoon Lee, Kyeongjin Ahn, Yubin Choi, Junho Lee, Meeyoung Cha and Kyung Ryul Park

KAIST STP