Working with the social workers and the participants themselves, the researchers mapped the social networks of participants and used their algorithm to find leaders with the most diverse set of connections, across different network clusters.

Facilitators from the social work research team then trained the chosen peer leaders on sexual health, HIV prevention, communication skills, leadership skills, and self-care. The peer leaders were asked to promote regular HIV testing and condom use through communication with their social ties at the drop-in center.

The research team found that youth enrolled in the AI-assisted strategy, dubbed CHANGE (CompreHensive Adaptive Network samplinG for social influencE), were significantly less likely to engage in unprotected sex than their peers enrolled in the observation-only group. The researchers also found that behaviors changed faster in the CHANGE group than in a group where the most popular youth were recruited as peer leaders. Most of the improvement for participants in CHANGE occurred by the one-month survey, while improvements in the "most popular" group werent seen until month three.

The speed in which we saw results in the CHANGE group is really important, said Bryan Wilder, a graduate student at SEAS and first author of the study. Not only does the rapid adoption of protective behaviors help to immediately reduce transmission of HIV in a high-risk population, but this population is also highly transient. Many of these young people will have left the center by the time a three-month intervention is completed so, you need to be able to reach as many people as possible within a short time period.

To the best of our knowledge, this is the first demonstration of the use of AI methods to optimize social network interventions for health, said Tambe. We hope that this project can provide general lessons about how AI research can be successfully employed for social good.

This strategy could be used to disseminate information within communities about nutrition, substance abuse and other public health crises that impact the most vulnerable people in our society, said Wilder.

The research was co-authored by Laura Onasch-Vera, Graham Diguiseppi, Chyna Hill and Eric Rice of the Suzanne Dworak-Peck School of Social Work and Center for Artificial Intelligence in Society at the University of Southern California; Amulya Yadav, of the College of Information Sciences and Technology at Pennsylvania State University; and Robin Petering, of Lens Co.

The research was funded by the Army Research Office (MURI W911NF1810208) and the California HIV/AIDS Research Program.

See the article here:

AI can help reduce the risk of HIV in high-risk communities - Harvard School of Engineering and Applied Sciences