Leveraging AI to Map SDG Coverage and Uncover Partnerships in Swiss Philanthropy

Maria Cristiana Tudor (), Lucia Gomez, Camilla Della Giovampaola, Hubert Halopé and Giuseppe Ugazio
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Maria Cristiana Tudor: University of Geneva
Lucia Gomez: University of Geneva
Camilla Della Giovampaola: University of Geneva
Hubert Halopé: University of Geneva

Chapter 9 in Artificial Intelligence for Sustainability, 2024, pp 175-206 from Springer

Abstract: Abstract The United Nations (UN) Sustainable Development Goals (SDGs) 2030 Agenda captures the complexity of the challenges humanity is facing to prevent irreversible climate outcomes. Advancing the SDGs requires coordinated partnership action between governments, civil society, and private sector actors. Philanthropy is one of the critical forces in promoting and implementing activities geared toward the SDGs, given its altruistic concern for the public good. Parallelly, Artificial Intelligence (AI) is a crucial technology allowing organizations to optimize decision-making through data-driven tools and insights. However, the role of AI in upgrading philanthropic efforts remains largely untapped. This chapter outlines how AI can support the philanthropic sector towards SDGs achievement by enhancing the creation of SDGs-aligned partnerships among Philanthropic Organizations (POs). A bottleneck that reduces efficient coordination between POs is the insufficient knowledge and visibility of the SDGs-aligned activities conducted by other agents. To address this bottleneck, we propose an AI approach using Natural Language Processing (NLP) for mapping SDG coverage by POs in the Swiss ecosystem. This aims to increase visibility between POs and facilitate partnership formation, thus synergizing SDGs achievement efforts through cooperation and resource pooling.

Keywords: Artificial Intelligence; Natural Language Processing; SDG17; Partnerships; Philanthropy (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-49979-1_9

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DOI: 10.1007/978-3-031-49979-1_9

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