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Robin Petering

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4

AAAI Conference 2021 Conference Paper

Clinical Trial of an AI-Augmented Intervention for HIV Prevention in Youth Experiencing Homelessness

  • Bryan Wilder
  • Laura Onasch-Vera
  • Graham Diguiseppi
  • Robin Petering
  • Chyna Hill
  • Amulya Yadav
  • Eric Rice
  • Milind Tambe

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.

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IJCAI Conference 2018 Conference Paper

Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth

  • Amulya Yadav
  • Bryan Wilder
  • Eric Rice
  • Robin Petering
  • Jaih Craddock
  • Amanda Yoshioka-Maxwell
  • Mary Hemler
  • Laura Onasch-Vera

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.

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AAMAS Conference 2018 Conference Paper

End-to-End Influence Maximization in the Field

  • Bryan Wilder
  • Laura Onasch-Vera
  • Juliana Hudson
  • Jose Luna
  • Nicole Wilson
  • Robin Petering
  • Darlene Woo
  • Milind Tambe

This work is aims to overcome the challenges in deploying influence maximization to support community driven interventions. Influence maximization is a crucial technique used in preventative health interventions, such as HIV prevention amongst homeless youth. Drop-in centers for homeless youth train a subset of youth as peer leaders who will disseminate information about HIV through their social networks. The challenge is to find a small set of peer leaders who will have the greatest possible influence. While many algorithms have been proposed for influence maximization, none can be feasibly deployed by a service provider: existing algorithms require costly surveys of the entire social network of the youth to provide input data, and high performance computing resources to run the algorithm itself. Both are crucial bottlenecks to widespread use of influence maximization in real world interventions. To address the above challenges, this paper introduces the CHANGE agent for influence maximization. CHANGE handles the end-toend process of influence maximization, from data collection to peer leader selection. Crucially, CHANGE only surveys a fraction of the youth to gather network data and minimizes computational cost while providing comparable performance to previously proposed algorithms. We carried out a pilot study of CHANGE in collaboration with a drop-in center serving homeless youth in a major U. S. city. CHANGE surveyed only 18% of the youth to construct its social network. However, the peer leaders it selected reached just as many youth as previously field-tested algorithms which surveyed the entire network. This is the first real-world study of a network sampling algorithm for influence maximization. Simulation results on real-world networks also support our claims.

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