Funding AI for Good: A Call for Meaningful Engagement with Hongjin Lin | AI & Equality Pub-Talk

🔗 Access paper: https://arxiv.org/abs/2509.12455

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 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.

Speaker: Hongjin Lin

Hongjin is a Ph.D. Candidate in Computer Science at Harvard University, advised by Professor Krzysztof Gajos. Her research lies at the intersection of AI and social impact, through both qualitative evaluation and technology development. Her work reveals power imbalances in AI for social good partnerships, where community organizations’ goals are often sidelined, and funding agendas play a prevalent role in determining project priorities and approaches. Drawing on community-collaborative design approaches, she is currently working on projects that support participation in local collective climate actions.

Hongjin was born and raised in Guangzhou, China, and moved to the US for her undergraduate degree in Mathematics and Computer Science at Occidental College. She received her master’s degree in Data Science from the London School of Economics. Before Harvard, she worked as a research fellow at Stanford Law School, developing and evaluating Machine Learning systems for environmental policy enforcement in partnership with the EPA. Outside of research, she has completed projects with nonprofits in China, the US, and Malawi, and worked as a data for development intern at UNDP in New York and Costa Rica. She is a dedicated yogi, dancer, community-living member, and a happy camper whenever she is away from her computer.

More: https://sites.google.com/g.harvard.edu/hongjinlin