New Report: Risky Analysis: Assessing and Improving AI Governance Tools
Report: From the Filing Cabinet to the Cloud: Updating the Privacy Act of 1974

COVID-19 and HIPAA: HHS’s Troubled Approach to Waiving Privacy and Security Rules for the Pandemic

The COVID-19 pandemic strained the U.S. health ecosystem in numerous ways, including putting pressure on the HIPAA privacy and security rules. The Department of Health and Human Services adjusted the privacy and security rules for the pandemic through the use of statutory and administrative HIPAA waivers. While some of the adjustments are appropriate for the emergency circumstances, there are also some meaningful and potentially unwelcome privacy and security consequences. At an appropriate time, the use of HIPAA waivers as a response to health care emergencies needs a thorough review. This report sets out the facts, identifies the issues, and proposes a roadmap for change.

WPF to discuss COVID-19 lessons learned and challenges ahead at OECD – Global Privacy Assembly Workshop

WPF Executive Director Pam Dixon will be presenting at an OECD and Global Privacy Assembly workshop on the risks, challenges, and potential solutions regarding the intersection of COVID-19 and the uses of identity in a global public health crisis. Event details: COVID-19 and privacy virtual Workshop on “The road to recovery: Lessons learned and challenges ahead”, hosted

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.