Funding AI for Good: A Call for Meaningful Engagement

Hongjin Lin, Anna Kawakami, Catherine D'Ignazio, Kenneth Holstein, Krzysztof Gajos

Abstract: Artificial Intelligence for Social Good (AI4SG) is a growing area that explores AI's potential to address social issues, such as public health. Yet prior work has shown limited evidence of its tangible benefits for intended communities, and projects frequently face inadequate community engagement and sustainability challenges. While existing HCI literature on AI4SG initiatives primarily focuses on the mechanisms of funded projects and their outcomes, much less attention has been given to the funding agenda and rhetoric that influences downstream approaches. Through a thematic analysis of 35 funding documents -- representing about $410 million USD in total investments, we reveal dissonances between AI4SG's stated intentions for positive social impact and the techno-centric approaches that some funding agendas promoted, while also identifying funding documents that scaffolded community-collaborative approaches for applicants. Drawing on our findings, we offer recommendations for funders to embed approaches that balance both contextual understanding and technical capacities in future funding call designs. We further discuss how the HCI community can positively shape AI4SG funding design processes.

External IDs:dblp:journals/corr/abs-2509-12455