Current Projects

Ethical Computing in Mobile & Wearable App Development

Katherine Shilton (Principal Investigator)
Adam Porter (Co-Principal Investigator)
Susan Winter (Co-Principal Investigator)
ICONS Project (http://www.icons.umd.edu/)

In this NSF-funded project, the research team studies academic and commercial software research and development (R&D) to discover factors that encourage discussion and action on ethical challenges. We will then incorporate findings into curricular materials for computer ethics by building interactive R&D simulations both for the classroom and for massive online open courseware (MOOCs). Project outcomes will answer the following questions:

  • What practices within mobile application research and development encourage discussion of, and decisions about, ethics?
  • How can these practices be incorporated into computer ethics education?
  • How do educational simulations based on these practices impact students’ learning and development practices?

National Science Foundation SES-1449351
Google Faculty Research Award

Values in a Future Internet Architecture

Katherine Shilton (Principal Investigator)
Jeff Burke, UCLA (Co-Principal Investigator)

Named Data Networking (NDN) is a long-term research effort to redesign the underlying architecture of the Internet. This project studies NDN’s impacts on social issues such as privacy, intellectual property, law enforcement, governance, and policy. The project investigates the distinction between values intended by developers in the NDN core architecture and values enacted in its implementation(s). Research questions include:

  1. How do values embedded in the NDN architecture become enacted in application design and use?
  2. What social issues are bound up in NDN technical problems?
  3. How can values-in-design perspectives help solve these technical problems?
  4. What interventions and strategies encourage values conversations within the technical work of infrastructure design?

The project addresses these questions using qualitative methods and targeted technical interventions. It uses a cooperative research approach, in which social scientists work alongside the NDN team of networking researchers. Project outcomes will include a detailed report on critical social, cultural, and economic considerations for the design of NDN, and future network architectures more generally. Other expected outcomes include technical changes to the NDN architecture based on this set of considerations and through cross-disciplinary dialogue.

National Science Foundation CNS-1421876

Privacy in Citizen Science

Katherine Shilton (Principal Investigator)
Jennifer Preece (Co-Principal Investigator)
Anne Bowser (Co-Principal Investigator)

Citizen science is a form of collaboration where members of the public participate in scientific research. Citizen science is increasingly facilitated by a variety of wireless, cellular and satellite technologies. Data collected and shared using these technologies may threaten the privacy of volunteers. This project will discover factors that lead to, or alleviate, privacy concerns for citizen science volunteers. The findings may support citizen science by exploring the privacy protection practices utilized by citizen science coordinators and volunteers. The results of this research will include best practices and policy guidelines for supporting privacy in citizen science. They will be published in a whitepaper distributed by the Woodrow Wilson International Center for Scholars and the United States Citizen Science Association, ensuring that broad audiences in public policy and in the citizen science community benefit from this work.

National Science Foundation SES-1450625

Fostering New Collaborations in Open Online Community Data Research

Susan Winter (Principal Investigator)
Brian Butler (Co-Principal Investigator)

Understanding research ethics for open online community data is an ongoing challenge. As part of a larger initiative to prototype a “data factory” for open online community data, our team is investigating research ethics challenges researchers experience when collecting, managing, and analyzing this type of data. This study will collect interviews with researchers engaged with open online community data focused on ethical challenges they have faced, open ethical questions, and what resources might help future researchers meet ethical challenges.

National Science Foundation IIS-1449188

Public Health, Nanotechnology and Mobility (PHeNoM): Understanding Design and Adoption of Lab-On-Chip Biometric Devices

An interdisciplinary project team at Cornell University (the PHeNoM project: http://www.news.cornell.edu/stories/2014/08/healthier-you-let-your-smartphone-call-it) is developing a smartphone attachment to read blood and saliva test strips, as well as applications to provide the results and guide users through the process. Our goal is to investigate the social values and impacts of lab-on-chip technologies in this mobile health context. Members of the EViD lab lead an ethnographic study of the social contexts in which the PHeNoM devices and bio-mobile diagnostics are designed and deployed. This work will determine sociotechnical factors which may impact adoption, adaption, or rejection of devices and diagnostics within medical and personal use contexts.

National Science Foundation CBET-1343058

Consumer Privacy Expectations in the Mobile Ecosystem

Kirsten Martin, George Washington University (Principal Investigator)

This project examines US consumers’ privacy expectations across mobile data contexts. We are conducting a series of surveys using factorial vignette methodology, in which respondents answer questions based on a series of hypothetical vignettes. This method allows us to examine multiple factors—e.g., changes in context and types of privacy violations—simultaneously by providing respondents with rich vignettes, which are systematically varied. The method supports the identification of the implicit factors and their relative importance in deciding that a situation meets or violates expectations of privacy. Study results will identify mobile contexts with similar privacy expectations (e.g., gaming, shopping, socializing, blogging, researching, etc), and identify the relative importance of data use and contextual factors in developing privacy expectations for specific mobile data contexts.

UMD ADVANCE Program