The long-simmering debate over facial recognition technology is taking on new urgency during the pandemic, as companies rush to pitch face-scanning systems to track the movements of Covid-19 patients. That's playing out in California, where state legislators on Tuesday will debate legislation that would regulate the use of the technology. Its most controversial element: It would permit companies and public agencies to feed people's facial data into a recognition system without their consent if there is probable cause to believe they've engaged in criminal activity. The bill isn't specifically meant for the coronavirus response, but if enacted, could shape the way that people with Covid-19 and their contacts are tracked and traced in the coming months. The legislation has won the support of Microsoft, but it has garnered opposition from more than 40 civil rights and privacy groups and from 18 public health scholars.

The rapid emergence and spread of the novel coronavirus, 2019-nCoV, has alarmed people around the world. While the possibility of a global pandemic is real, people can take some solace in the fact that public health officials have at their disposal an array of powerful data collection and analytics techniques that previous generations lacked. The virus, which causes a pneumonia-like illness that's quite similar to the Severe Acute Respiratory Syndrome (SARS) outbreak 2003 that killed 800 people, appears to have jumped into the human biome at an exotic meet market in Wuhan, China, where delicacies like bats and snakes were sold to the public. But what makes 2019-nCoV dangerous is its ability to spread from human to human, and that's how more than 17,000 Chinese citizens have gotten sick. However, before Chinese authorities could quarantine Wuhan and surrounding areas, infected individuals were allowed to travel around the world, and today individuals in 20 countries have been reported to be infected with 2019-nCoV, which the World Health Organization (WHO) last week declared a global health emergency.

"Machine learning is very good at identifying patterns in the data, such as risk factors that might identify zip codes or cohorts of people that are …