The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is
enormous, particularly in the areas of healthcare, conservation and public safety and security.
Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness
about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these realworld problems are characterized by uncertainties about social network structure and influence
models, and previous research in AI fails to sufficiently address these uncertainties, as they make
several unrealistic simplifying assumptions for these domains.
This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the
design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks (e.g., uncertainty in social network
structure, evolving network state, and availability of nodes to get influenced). These algorithms
utilize techniques from sequential planning problems and social network theory to develop new
kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV
among actual homeless youth in Los Angeles. This represents one of the first-ever deployments
of computer science based influence maximization algorithms in this domain. Our results show
that our AI algorithms improved upon the state-of-the-art by ∼ 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons
that can be gleaned for future deployment of such algorithms. The positive results from these
deployments illustrate the enormous potential of AI in addressing societally relevant problems.