70 Am. U. L. Rev. 1765 (2021).
* Anne L. Washington, PhD, is an Assistant Professor of Data Policy at New York University. Lauren Rhue, PhD, is an Assistant Professor of Information Systems at the University of Maryland’s Robert H. Smith School of Business. This work draws on a presentation we made at the Future of Privacy Forum’s International Tech & Data Conference in October 2020. We would like to thank Charles T. Stokes, 2019 graduate of Stevenson University in Baltimore, Maryland, for research assistance.
Predictive data technology designed to contain the COVID-19 pandemic was not as successful as promised. Data-centric solutions to providing testing and tracing did little to limit the virus’s spread in part because they served only the most visible parts of society. This Article argues for more robust solutions to protect individuals’ privacy—whether those individuals are currently visible or invisible to pandemic technology—if pandemic technology is to provide the universal coverage necessary for a public health emergency, such as the COVID-19 pandemic. First, we contend that current pandemic data technology operates under rigid technical and social assumptions that thwart participation from all population groups. Second, we demonstrate that the organizations associated with pandemic data technology have financial incentives that could be in opposition to protecting anyone susceptible to the virus. Third, we consider how the need for someone to protect data to allow for medically necessary access to data could be an onramp for a pilot implementation of legal theory on information fiduciaries. Finally, we offer two tangible policy suggestions: conflict-of-interest notices released as open data and a public health fiduciary that has legal responsibility to protect data relevant to epidemiological outbreaks. A public health fiduciary working in the public interest would be more likely to gather sufficiently accurate data than would a fiduciary working within the organizations collecting data themselves. Technology has a vital role to play in managing the pandemic, but in the hands of some organizations, it may encourage behavior that counters public health goals. Trusted data technology solutions in conjunction with predictive epidemiology models could contribute to reducing the spread of the virus more holistically and with fewer privacy-related consequences.