Deploying AI in the Time of COVID: Risks, Benefits, and Tradeoffs

Panelists: Rachel Greenstadt, New York University; Lorena Jaume-Palasi, The Ethical Tech Society; Ben Zhao, The University of Chicago; Josh Saxe, Sophos


The COVID-19 pandemic has had an extreme effect on our lives. Using machine learning or artificial intelligence to gain insights about the disease could help governments and corporations to reduce risks. However, given the uncertainty associated with the disease, and the speed at which events are happening it is an open question whether or not current techniques are both sufficient to address the problem and capable of being deployed without undue risk. In this panel, we will discuss the security and privacy challenges surrounding AI in such an environment. Together with experts from academia, industry, and civil society, we will explore questions such as: What can AI bring to this pandemic? What impact has COVID-19 had on the security and privacy landscape and does AI have any role in addressing these new challenges? Is our current understanding of how to secure AI/ML models adequate to address the challenges of collecting COVID-19 related data? If not, how should we address the trade-offs between privacy and security risks and public health? Does the uncertain scenario open the door to new attacks that may affect how we confront the pandemic? What effect might data-driven policies have on society?

@conference {257055,
title = {Deploying {AI} in the Time of {COVID}: Risks, Benefits, and Tradeoffs},
year = {2020},
publisher = {USENIX Association},
month = aug