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On-demand Work Recommendation for Disadvantaged Youth Using Smartphones

This project focused on creating on-demand work matching for at-risk young adults, a novel initiative for workforce development and employment through mobile and recommendation technology combined with a nine-month training program called Night Owl Bakers (NOB) developed by the Latham Street Commons. 

Pittsburgh’s at-risk young adults experience greater degrees of economic disparity due to structural barriers between them and economic opportunities and experience devastatingly high levels of violence, physical, and emotional abuse. While trauma and its effects are not limited to youth in poorer communities, the consequences for youth living in neighborhoods with multi-layered stressors can be more challenging to resolve. There are also a number of external factors that perpetuate inequality and lack of civic engagement, including access to quality education and socially enriching learning experiences. 

To evaluate real-world data, the team developed a set of algorithms for predicting the location and routes of parties involved in the work matching process. The results showed that the predicted location of participants in Pittsburgh was on average within one mile from the actual location of those people and the precited location of people in the Bay Area was within two miles from the actual location. Researchers aim to build an intelligent system (both backend and mobile application) to support the recommendation of on-demand work opportunities to the graduates of the Night Owl Bakers program that match individuals’ skills, preferences, location, and environment.

The team worked to build an intelligent system (both backend and mobile application) to support recommendation of on-demand work opportunities to the graduates of the NOB program that match individuals skills, preferences, location, and environment. The NOB program provides a holistic approach to self-identity and employment preparedness for young adults between the ages 18-22 aging out of the foster care system. The 9-month curriculum has 3 phases that progress from a series of cross-disciplinary, hands-on experiences in business and financing, technology and science, food literacy and sourcing, and self-care and mindfulness. The program is designed to prepare participants for the future so they can experience a fuller sense of who they are, what they are a part of, and that they are capable of joining the workforce prepared.

The project system uses smartphones to identify and recommend relevant short-term work opportunities for the youth to apply their new skills. An example scenario is Tisha, an 18 year-old, aging out of foster care. Tisha is trained through the NOB program and displays a high level of initiative, friendly disposition and loves interacting with technology and social media. One day, she gets notified about an opportunity to deliver bread for an upcoming neighborhood event through our app. She accepts the request and delivers the bread. Next day, Tisha is notified that Duolingo, a tech company needs a part-time worker to help with a data analysis task. Since Tisha is trained and vetted in data analytics, she is accepted for the work after she expresses interest through the app.

The team anticipates that in five years, our on-demand work system will be a social enterprise model for others and serve as a self-sustaining source of workforce training