Community-based designs of Human-centered AI and Public Policy: Towards Promoting Global Inclusion, Resilience, and Sustainability
The field of computational social science has been traditionally focused on analyzing large-scale human and social dynamics in social media. However, millions of people from historically disadvantaged communities, harmed by climate change, structural inequalities, and human rights violations, remain missing from the digital databases, Internet traces, census records, and mainstream policy design processes. While data-intensive algorithms could help improve the livelihood of these at-risk populations, the key challenge is that their prejudiced designs overlook the interconnected nature of colonial, socioeconomic, institutional, and ecological processes that govern our world and amplify algorithmic harms.
In this talk, I will present a data science research program demonstrating how we can bring digitally invisible communities to the center of designing data-intensive public interest computing systems for collaborative public policy decision-making concerning sustainable development. During the talk, I will discuss resilience and adaptation mechanisms required to address climate disasters and agricultural market failures that have led to over 300000 farmer suicides, food systems crises, and forced displacements of women and children in the Global South. Finally, I will showcase how human-AI collaboration system designs, coupled with participatory and remote sensing satellite datasets, can advance equitable policymaking and make socio-technical systems more resilient to the perils of sustainability.
By studying the interconnected dynamics of socioeconomic and environmental processes and their impact on digitally missing and underserved communities, this scholarship broadens the horizon of computational social science research beyond the Internet ecosystems. It has already led to the informed data science research and policy program in Data-driven Humanitarian Mapping that convenes a global community of stakeholders from industry, academia, NGOs, and governments to tackle overarching sustainability challenges posed by climate change and the COVID-19 pandemic.
Neil Gaikwad is a doctoral scholar at MIT, specializing in Human-centered AI and Public Policy for Sustainable Systems. He develops computational and data science lenses to address public policy issues concerning sustainability and international development. This research focuses on the community-based design of data-intensive public interest computing systems to advance equitable public policy interventions for improving the livelihood of historically disadvantaged populations affected by climate change, structural inequalities, and human rights violations. Neil’s scholarship has resulted in publications at AI & HCI conferences, talks at UN and EU global policy forums, environmental art exhibitions, and featured articles in the New York Times, New Scientist, WIRED, Wall Street Journal. He has mentored over 20 students who pursued careers in research and published influential scholarship that has shifted the discourse on AI fairness. His research, teaching, leadership, and commitment to diversity, inclusion and belonging have been recognized with Facebook Ph.D. Fellowship in Computational Social Science, MIT Human Rights & Technology Fellowship, William Asbjornsen Albert Memorial Science & Engineering MIT Fellowship, MIT Graduate Teaching Award, and Karl Taylor Compton Prize (highest student award of MIT). Neil earned a master’s degree from the School of Computer Science at Carnegie Mellon University.