Artificial intelligence holds tremendous promise to improve
human well-being. However, AI techniques are typically developed for the benefit of those with access to technological
and financial resources. A critical but understudied question
is how AI can benefit marginalized communities who lack
such resources. Governments and communities worldwide
use a range of interventions to tackle social problems such as
homelessness and disease, improving access to opportunity
for underserved populations. My research develops machine
learning and optimization methods to empower such interventions, which are almost always deployed with limited resources and limited information. Maximizing impact in this
context requires algorithmic approaches which span the full
pipeline from data to decisions. Computationally, we aim
to target scarce resources for greatest effect under substantial uncertainty. The ultimate goal is to include communities
worldwide in the benefits of AI progress