Join us from August 24 through December 14 at the AI for Social Impact Seminar Series. This seminar series will explore how artificial intelligence can contribute to solving social problems.
Artificial intelligence is poised to play an increasingly large role in societies across the world. Accordingly, there is a growing interest in ensuring that AI is used in a responsible and beneficial manner. A range of perspectives and contributions are needed, spanning the full spectrum from fundamental research to sustained deployments.
This seminar series will explore how artificial intelligence can contribute to solving social problems. For example, what role can AI play in promoting health, access to opportunity, and sustainable development? How can AI initiatives be deployed in an ethical, inclusive, and accountable manner?
Recording of the talk: https://www.youtube.com/watch?v=YOFTtdT5rww
Eric Rice (University of Southern California)
Using AI to Augment HIV Prevention Interventions for Homeless Youth: Results from a Large Clinical Trial
Abstract: Each year, there are nearly 4 million youth experiencing homelessness (YEH) in the United States with HIV prevalence ranging from 3 to 11.5%. Peer change agent (PCA) models for HIV prevention have been used successfully in many populations, but there have been notable failures. In recent years, network interventionists have suggested that these failures could be attributed to PCA selection procedures. The change agents themselves who are selected to do the PCA work can often be as important as the messages they convey. To address this concern, we tested a new PCA intervention for YEH, with three arms: (1) an arm using an artificial intelligence (AI) planning algorithm to select PCA, (2) a popularity arm—the standard PCA approach—operationalized as highest degree centrality (DC), and (3) an observation only comparison group (OBS). We tested this approach with 704 youth between 2017 and 2019. We found that PCA models that promote HIV testing, HIV knowledge, and condom use are efficacious for YEH. Both the AI and DC arms showed improvements over time. AI-based PCA selection led to better outcomes and increased the speed of intervention effects. Specifically, the changes in behavior observed in the AI arm occurred by 1 month, but not until 3 months in the DC arm. Given the transient nature of YEH and the high risk for HIV infection, more rapid intervention effects are desirable.
Bio: Eric Rice is an associate professor and the founding co-director of the USC Center for Artificial Intelligence in Society, a joint venture of the USC Suzanne Dworak-Peck School of Social Work and the USC Viterbi School of Engineering. Rice received a BA from the University of Chicago, and an MA and PhD in Sociology from Stanford University. He was a postdoctoral fellow at the University of California, Los Angeles. He joined the USC faculty in 2009. In 2012 he received the John B. Reid Early Career Award through the Society for Prevention Research. He specializes in social network science and theory, as well as community-based research. His primary focus is on youth experiencing homelessness and how issues of social network influence may affect risk-taking behaviors and resilience. For several years he has been working with colleague Milind Tambe to merge social work science and AI, seeking novel solutions to major social problems such as homelessness and HIV. Rice is the author of more than 100 peer-reviewed articles in such publications as the American Journal of Public Health, AIDS and Behavior, the Journal of Adolescent Health, Pediatrics, Child Development, and the Journal of the Society for Social Work Research. He is the recipient of grants from the National Institute of Mental Health, the California HIV/AIDS Research Program, the Army Research Office and other agencies.