USC researchers create algorithm to optimize substance abuse intervention groups

When it comes to fighting substance abuse, research suggests the company you keep can make the difference between recovery and relapse. So, while group intervention programs can play an important role in preventing substance abuse, especially in at-risk populations such as homeless youth, they can also inadvertently expose participants to negative behaviors. Now, researchers from the USC Center for Artificial Intelligence in Society have created an algorithm that sorts intervention program participants - who are voluntarily working on recovery - into smaller groups, or subgroups, in a way that maintains helpful social connections and breaks social connections that could be detrimental to recovery. "We know that substance abuse is highly affected by social influence; in other words, who you are friends with," says Aida Rahmattalabi, a USC computer science graduate student and lead author of the study. "In order to improve effectiveness of interventions, you need to know how people will influence each other in a group."