{"type":"ScholarlyArticle","title":"Machine Learning Driven Aid Classification for Sustainable Development","authors":[{"name":"Junho Lee","slug":"junho-lee","given":"Junho","family":"Lee","affiliation":"Korea Advanced Institute of Science and Technology (KAIST) , South Korea; Inter-American Development Bank (IDB) , United States"},{"name":"Hyeonho Song","slug":"hyeonho-song","given":"Hyeonho","family":"Song","affiliation":"Korea Advanced Institute of Science and Technology (KAIST) , South Korea; Institute for Basic Science (IBS) , South Korea"},{"name":"Dongjoon Lee","slug":"dongjoon-lee","given":"Dongjoon","family":"Lee","affiliation":"Korea Advanced Institute of Science and Technology (KAIST) , South Korea"},{"name":"Sundong Kim","slug":"sundong-kim","given":"Sundong","family":"Kim","affiliation":"Gwangju Institute of Science and Technology (GIST) , South Korea"},{"name":"Jisoo Sim","slug":"jisoo-sim","given":"Jisoo","family":"Sim","affiliation":"Korea Advanced Institute of Science and Technology (KAIST) , South Korea"},{"name":"Meeyoung Cha","slug":"meeyoung-cha","given":"Meeyoung","family":"Cha","affiliation":"Institute for Basic Science (IBS) , South Korea; Korea Advanced Institute of Science and Technology (KAIST) , South Korea"},{"name":"Kyung-Ryul Park","slug":"kyung-ryul-park","given":"Kyung-Ryul","family":"Park","affiliation":"Korea Advanced Institute of Science and Technology (KAIST) , South Korea"}],"abstract":"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":"10.24963/ijcai.2023/670","url":"https://ijcai.org/proceedings/2023/670","pdf":"https://ijcai.org/proceedings/2023/0670.pdf","markdown":"https://ijcai.org/proceedings/2023/670.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/670","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":670,"pages":{"first":6040,"last":6048},"keywords":["AI for Good: Multidisciplinary Topics and Applications","AI for Good: Humans and AI","AI for Good: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Alsayyad and Nawar, 2021] Amina Said Alsayyad and Abdel-Hameed Hamdy Nawar. 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