  Youth experiencing homelessness (YEH) are subject to substantially greater
risk of HIV infection, compounded both by their lack of access to stable
housing and the disproportionate representation of youth of marginalized
racial, ethnic, and gender identity groups among YEH. A key goal for health
equity is to improve adoption of protective behaviors in this population. One
promising strategy for intervention is to recruit peer leaders from the
population of YEH to promote behaviors such as condom usage and regular HIV
testing to their social contacts. This raises a computational question: which
youth should be selected as peer leaders to maximize the overall impact of the
intervention? We developed an artificial intelligence system to optimize such
social network interventions in a community health setting. We conducted a
clinical trial enrolling 713 YEH at drop-in centers in a large US city. The
clinical trial compared interventions planned with the algorithm to those where
the highest-degree nodes in the youths' social network were recruited as peer
leaders (the standard method in public health) and to an observation-only
control group. Results from the clinical trial show that youth in the AI group
experience statistically significant reductions in key risk behaviors for HIV
transmission, while those in the other groups do not. This provides, to our
knowledge, the first empirical validation of the usage of AI methods to
optimize social network interventions for health. We conclude by discussing
lessons learned over the course of the project which may inform future attempts
to use AI in community-level interventions.
