Every year, 3.5 million people in the US experience homelessness, with 1 in 30 children becoming homeless. Despite numerous government-sponsored programs and efforts by nonprofit organizations, many homeless people live in abject conditions. This research re-envisions smart city technologies to best serve those in need of access to basic resources including food, shelter and medical services. The proposed infrastructure will connect the currently disjoint efforts of public services, NGOs and private citizens, and use population-modeling and planning algorithms to match the varying and unpredictable supply with those who need it. In pursuit of the overarching goal of collecting and delivering services to maximize social welfare, this research will make advances in the science of population modeling, the analysis and design of human-centered planning algorithms, and technological challenges including secure and privacy-aware sensing modalities and mobile technologies.
As part of a human-centered design approach, interviews and observations will be conducted to understand user needs, and design a system that multiple stakeholders can use to report their needs and extra supply. This collected data will be used by non-profit organizations to strategically distribute resources. The real-world stakeholders such as food banks, food pantries, shelters, street medicine teams, and food rescue organizations will be closely involved in the design and evaluation process.
This research is high-risk and high-reward, and appropriate for EAGER. Failure means that the resulting planning algorithms will make unfair decisions and prioritize a few organizations or donors, or will make fair, but inefficient allocation decisions, which will endanger social justice and community well-being. Success will improve both efficiency of resource distribution and the quality of life of underserved populations in the United States. The completion of the project will produce 1) algorithms for optimal resource allocation that are both efficient and aware of human-in-the-loop concerns, and which can be used for other functions including disaster-response, and 2) communication infrastructure for non-profit organizations, volunteers, and populations in need, to coordinate other service activities. The project has potential for great societal impact: it will make charitable donations convenient and inexpensive for those with supply power, increasing the volume of donations and thereby reducing wastage. The outcome will be an improved realization of the philanthropic potential of the increasingly sharing nature of the American economy.
Abstract
Min Kyung Lee
Min Kyung Lee is an assistant professor in the School of Information at the University of Texas at Austin. Dr. Lee has conducted some of the first studies that empirically examine the social implications of algorithms' emerging roles in management and governance in society, looking at the impacts of algorithmic management on workers as well as public perceptions of algorithmic fairness. She has proposed a participatory framework that empowers community members to design matching algorithms for their own communities. Her current research on human-centered AI is inspired by and complements her previous work on social robots for long-term interaction, seamless human-robot handovers, and telepresence robots. Dr. Lee is a Siebel Scholar and has received the Allen Newell Award for Research Excellence, research grants from NSF and Uptake, and five best paper awards or honorable mentions in venues such as CHI, CSCW, DIS and HRI. She is an associate editor of the ACM Transactions on Human-Robot Interaction. Her work has been featured in media outlets such as the New York Times, New Scientist, Washington Post, MIT Technology Review and CBS. She received a PhD in Human-Computer Interaction and an MDes in Interaction Design from Carnegie Mellon University.
Performance Period: 09/01/2016 - 08/31/2018
Institution: Carnegie-Mellon University
Award Number: 1651566
Core Areas:
Community Planning, Education, and the Workforce