Today, much of fire departments' work focuses on community risk reduction (CRR). The CRR paradigm will leverage new data-driven risk prediction and management (DDRPM) tools to predict and respond to community risks. Yet, designing DDRPM tools to realize this vision requires a deep understanding of current work practices and the potential future impacts of such tools on labor, work, and communities. This research will develop and design new DDRPM tools that anticipate and manage various community risks to which fire departments are increasingly required to respond. We will achieve these goals by using ethnographic approaches, human-centered design methods, and the development of a sociotechnical theory of risk work. This project will directly benefit the study sites by translating research outcomes into practical insights.

An approach to designing social services, called "systems of care," organizes services around individuals and their specific context. The goal of this project is to (1) advance accountable software systems through developing generalizable and localizable practices for exploring how to identify systemic sources of bias, (2) improve public service provisioning outcomes, and (3) minimize disparities from biased program outcomes. The project will leverage social theories, computational methods, and ethnographic approaches to translate social science findings into software practices based on a partnership with civic tech and non-profit organizations. The project outcome will enhance accountability in designing software systems that will be used as part of a larger socio-technical system of care.

This research develops a foundational visualization tool for understanding how civic technologies are used and how information inequalities manifest in a city. By leveraging Boston's 311 data (i.e., residents' reports of non-emergency civic issues such as traffic issues), we explore how and why public services are unevenly distributed across neighborhoods, which leads to the creation of information deserts. Then, we design a visualization system that demonstrates the relationships between socio-economic factors and the information deserts of civic issues.

This project explores the socio-material dynamics of risk work and risk information seeking during public health crises. We identify the socially-constructed dimensions of how people perceive risks, and further dive into how risk information communicated through social and technological systems shapes their risk behaviors, using ethnographic approaches.

Organizations and individuals provide information to multiple systems. These information practices shaped separately by different actors lead to fragmented information, especially in disability services. The complex interactions between actors and systems make it difficult for information managers to understand the flow of information. Our research will focus on Medicaid services for people with developmental disabilities in Virginia, aiming to provide a comprehensive understanding of the information ecology. We will identify information providers' key challenges in serving the target population and provide recommendations for improving information accessibility. By doing so, we hope to help people with disabilities and their families who currently bear the burden of navigating multiple sources to access necessary information.

Artificial intelligence (AI) is increasingly playing more active roles than ever on Event-based Social Network (EBSN) platforms and is expected to alter local dynamics at scale. This pilot project aims to uncover how human-AI collaboration happens at the community level by implementing and simulating machine learning algorithms at the ecological level using local gathering data from Meetup.com.

This project builds a map-based archival platform that presents property acquisition documents of the City of Asheville, North Carolina, from the urban renewal in the 1960s and '70s. The platform focuses on the searchability and navigability of the digitized archival collection, so former residents, archivists, researchers, and citizens can access necessary information better. This platform ultimately reconstructs a virtual neighborhood where people can share their memories and tell stories. This project leverages computational methods for curating historical data, GIS technologies for dynamic visualization, and value-sensitive design for designing the system.

During a global pandemic, many jurisdictions have used civic technologies such as 311 systems to manage non-emergency issues such as the violation of mask mandates. While civic issues that residents need to communicate are similar across the regions, jurisdictions' imposed communication methods vary significantly due to their policies, legacy systems, and institutional logics. By working with our community partner, Connected DMV, this planning project aims to understand how local governments in the D.C. area implement communication technologies during a crisis, and how people reappropriate the systems. Ultimately, this project seeks to suggest a regional technology strategy to cope with regional issues such as pandemics.