Can Machine Learning Create an Advocate for Foster Youth?

ABSTRACT

Statistics are bleak for youth aging out of the United States foster care system. They are often left with few resources, are likely to experience homelessness, and are at increased risk of incarceration and exploitation. The Think of Us platform is a service for foster youth and their advocates to create personalized goals and access curated content specific to aging out of the foster care system. In this article, we propose the use of a machine learning algorithm within the Think of Us platform to better serve youth transitioning to life outside of foster care. The algorithm collects and collates publicly available figures and data to inform caseworkers and other mentors chosen by the youth on how to best assist foster youth. It can then provide valuable resources for the youth and their advocates targeted directly toward their specific needs. Finally, we examine machine learning as a support system and aid for caseworkers to buttress and protect vulnerable young adults during their transition to adulthood.

KEYWORDS:

• Information systems
• Internet
• smart environments
• social networking

Acknowledgments

We thank the foster youth, foster alumni, and caseworkers who have offered their stories and contributed to the beginning of Think of Us Platform database. We also thank Sixto Cancel for his vision and support in the addition of machine learning to the Think of Us Platform, and Daniel Torraca, for his design work on the platform.