Rates of suicidal ideation, suicide plans, and suicide attempts are highest among 18-25 year olds in the U.S. (1).  About one-quarter of college students thought about suicide in the past year, and 9% reported making an attempt (2).  Gatekeeper training is an evidence-based suicide prevention program which trains individuals to monitor their social networks (e.g., friends, family, peers, soldiers, caregivers) for warning signs of suicides and to then recommend help for persons in need (3).  In current implementation, gatekeeper interventions often do not use the available social network structure data or rely upon self-selection, potentially limiting the opportunity to identify gatekeepers who could yield as much coverage as possible (4-5).

Our project is using an in-person social network based approach with the aid of a coverage algorithm to improve the effectiveness and reach of gatekeeper interventions in real world social networks of college students.  The selection of gatekeepers must consider the uncertainty of the availability of potential candidates (e.g., schedule, interest, etc.), along with the best potential gatekeepers based on network connection.

When tested on real social network instances, existing algorithms yield unfair solutions, such as the marginalization of racial minorities. As a result, we are investigating a novel solution methodology that incorporates fairness constraints while efficiently solving for near-optimal solutions when selecting gatekeeper candidates.