Funding AI for Good: A Call for Meaningful Engagement

TL;DR

The paper analyzes AI4SG funding documents to reveal how rhetoric and priorities in funding calls shape downstream project approaches. Using reflexive thematic analysis of 35 documents totaling about $410 million, it identifies a spectrum from techno-centric to balanced framing, with a focus on outcomes, eligibility, and funder support. Key contributions include revealing dissonances between AI4SG's social-impact promises and techno-centric funding practices, proposing funding-call designs that foreground contextual understanding and community co-leadership, and outlining roles for the HCI community in shaping funding processes. The work highlights the need for meaningful community engagement early in funding design to improve adoption, sustainability, and epistemic justice in AI4SG initiatives. Practically, it offers concrete recommendations for funders and a research agenda for HCI scholars to mediate between communities, funders, and implementers.

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.

Figures (7)

• Figure 1: AI4SG aims to bring about positive, long-lasting social impact. Prior literature emphasizes that impactful innovations are the result of a balance of technology capacities and contextual understanding (right panel). A bias towards technology capacities leads to innovations without a real-world positive impact (left panel). We use this spectrum as a lens to investigate how current AI4SG funding documents orient towards more techno-centric or balanced approaches.
• Figure 2: Themes related to rhetoric of motivations for AI4SG that lie across the spectrum between a more techno-centric versus balanced approach. The positions of the themes do not represent their prevalence.
• Figure 3: Themes related to funders' expected deliverables for AI4SG projects that lie across the spectrum between a more techno-centric versus balanced approach. The positions of the themes do not represent their prevalence.
• Figure 4: Themes related to edibility criteria of AI4SG funding calls that lie across the spectrum between a more techno-centric versus balanced approach. The positions of the themes do not represent their prevalence.
• Figure 5: Themes related to the support that AI4SG funders offered that lie across the spectrum between a more techno-centric versus balanced approach. The positions of the themes do not represent their prevalence.