Classifying and Tracking International Aid Contribution Towards SDGs

TL;DR

This paper presents an AI-based approach to classify and analyze international aid data related to SDGs, improving accuracy, revealing trends, and aiding policymakers in making data-driven decisions.

Contribution

The paper introduces a novel AI model that enhances aid classification accuracy by integrating SDG semantics and prior language model knowledge, addressing data heterogeneity and bias.

Findings

Improved classification accuracy of aid data.

Revealed hidden temporal trends in aid contributions.

Provided insights for policy decision-making.

Abstract

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.