COVID-19

Governing Data for Development: Trends, Challenges, and Opportunities

The World Privacy Forum is pleased to announce its work on a new project with the Center for Global Development (CGD). This project, Governing Data for Development, is led by CGD, with WPF’s Executive Director Pam Dixon as co-chair of the project working group with co-chair and Oxford professor Benno Ndulu, who is also the former Governor of the Central Bank of Tanzania. The project, which has been underway for a year, has produced its first report, which is a scoping report based on interviews with key stakeholders. This blog post, which provides background on the project and links to the first project report, is being jointly posted at WPF and CGD.

World Health Organization updates its data sharing principles; WPF participant in external expert advisory group

This summer, the World Privacy Forum served as a member of the World Health Organization’s External Expert Group on Data Principles. We are pleased to announce that WHO has now published its updated data principles and data sharing policy, as of October 2020.  While there are additional items that WPF would like to address in

K-12 schools during the pandemic: New National Academies of Science publication discusses unprecedented challenges

The COVID-19 pandemic has presented unprecedented challenges to the nation’s K-12 education system. These challenges certainly include the impacts of school closures, and the range of multi-layered, complex questions of whether to reopen school buildings and how to operate them safely if they do reopen. The pandemic has also highlighted significant fault lines in the

Africa’s Rising Leadership in Privacy: breaking new ground before and during the COVID-19 crisis

Numerous African countries, having passed new privacy laws during and after the time the GDPR was being negotiated, have broken new ground by advancing privacy thought in new and important ways which stretch past the boundaries of the GDPR and contextualize privacy for African contexts. The COVID-19 crisis represents a major test of some of the new data governance systems.

Face Recognition and Face Masks:  Accuracy of face recognition plummets when applied to mask-wearers; NIST report 

NIST has published its first report regarding face recognition algorithms and the wearing of face masks. The report quantifies how one-to-one face recognition systems perform when they are utilized on images of diverse people wearing a variety of mask types and colors. The study found that pre-COVID-19 FR algorithms have substantial error rates, some reaching as high as 50 percent for false non-match rates.