A Peer-Led, Artificial Intelligence–Augmented Social Network Intervention to Prevent HIV Among Youth Experiencing Homelessness

Background: Youth experiencing homelessness (YEH) are at elevated risk of HIV/AIDS and disproportionately identify as racial, ethnic, sexual, and gender minorities. We developed a new peer change agent (PCA) HIV prevention intervention with 3 arms: (1) an arm using an artificial intelligence (AI) planning algorithm to select PCAs; (2) a popularity arm, the standard PCA approach, operationalized as highest degree centrality (DC); and (3) an observation-only comparison group. Setting: A total of 713 YEH were recruited from 3 drop-in centers in Los Angeles, CA. Methods: Youth consented and completed a baseline survey that collected self-reported data on HIV knowledge, condom use, and social network information. A quasi-experimental pretest/posttest design was used; 472 youth (66.5% retention at 1 month postbaseline) and 415 youth (58.5% retention at 3 months postbaseline) completed follow-up. In each intervention arm (AI and DC), 20% of youth was selected as PCAs and attended a 4-hour initial training, followed by 7 weeks of half-hour follow-up sessions. Youth disseminated messages promoting HIV knowledge and condom use. Results: Using generalized estimating equation models, there was a significant reduction over time (P < 0.001) and a significant time by AI arm interaction (P < 0.001) for condomless anal sex act. There was a significant increase in HIV knowledge over time among PCAs in DC and AI arms. Conclusions: PCA models that promote HIV knowledge and condom use are efficacious for YEH. Youth are able to serve as a bridge between interventionists and their community. Interventionists should consider working with computer scientists to solve implementation problems.