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.

Interventions to reduce violence among homeless youth are difficult to implement due to the complex nature of violence. However, a peer-based intervention approach would likely be a worthy approach as it has been shown that individuals who interact with more violent individuals are more likely to be violent, suggesting a contagious nature of violence. We propose Uncertain Voter Model to represent the complex process of diffusion of violence over a social network, that captures uncertainties in links and time over which the diffusion of violence takes place. Assuming this model, we define Violence Minimization problem where the task is to select a predefined number of individuals for intervention so that the expected number of violent individuals in the network is minimized over a given time-frame. We extend the problem to a probabilistic setting, where the success probability of converting an individual into non-violent is a function of the number of “units” of intervention performed on them. We provide algorithms for finding the optimal intervention strategies for both scenarios. We demonstrate that our algorithms perform significantly better than interventions based on popular centrality measures in terms of reducing violence.

This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key "seed" nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ~160% in terms of information spread about HIV among homeless youth.

Homeless youth are prone to HIV due to their engagement in high risk behavior. Many agencies conduct interventions to educate/train a select group of homeless youth about HIV prevention practices and rely on word-of-mouth spread of information through their social network. Previous work in strategic selection of intervention participants does not handle uncertainties in the social network’s structure and in the evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision support system to aid the agencies in this task. PSINET includes the following key novelties: (i) it handles uncertainties in network structure and evolving network state; (ii) it addresses these uncertainties by using POMDPs in influence maximization; (iii) it provides algorithmic advances to allow high quality approximate solutions for such POMDPs. Simulations show that PSINET achieves ∼60% more information spread over the current state-of-the-art. PSINET was developed in collaboration with My Friend’s Place (a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.