Algorithmic Social Intervention

TL;DR

This paper introduces 'algorithmic social intervention' as a new research area focused on applying algorithmic techniques to improve social and behavioral interventions under resource and data constraints, with applications in public health.

Contribution

It defines the technical challenges of decision making under uncertainty in social interventions and presents algorithms optimized for social network interventions, especially in HIV prevention.

Findings

Algorithms improved HIV prevention strategies in pilot tests.

Preliminary results show substantial improvements over existing approaches.

Addresses decision making under uncertainty in resource-limited settings.

Abstract

Social and behavioral interventions are a critical tool for governments and communities to tackle deep-rooted societal challenges such as homelessness, disease, and poverty. However, real-world interventions are almost always plagued by limited resources and limited data, which creates a computational challenge: how can we use algorithmic techniques to enhance the targeting and delivery of social and behavioral interventions? The goal of my thesis is to provide a unified study of such questions, collectively considered under the name "algorithmic social intervention". This proposal introduces algorithmic social intervention as a distinct area with characteristic technical challenges, presents my published research in the context of these challenges, and outlines open problems for future work. A common technical theme is decision making under uncertainty: how can we find actions which will impact a social system in desirable ways under limitations of knowledge and resources? The primary application area for my work thus far is public health, e.g. HIV or tuberculosis prevention. For instance, I have developed a series of algorithms which optimize social network interventions for HIV prevention. Two of these algorithms have been pilot-tested in collaboration with LA-area service providers for homeless youth, with preliminary results showing substantial improvement over status-quo approaches. My work also spans other topics in infectious disease prevention and underlying algorithmic questions in robust and risk-aware submodular optimization.