The Volokh Conspiracy
Mostly law professors | Sometimes contrarian | Often libertarian | Always independent
If you've been paying attention to press and academic studies in recent years, you know one thing about face recognition algorithms. They're biased against women and racial minorities. Actually, you've probably heard they're racist. So says everyone from the MIT Technology Review and Motherboard to the ACLU and congressional Democrats.
There's just one problem with this consensus. It's wrong. And wrong in a way that has dangerous consequences. It's distorting laws all around the country and handing the global lead in an important new technology to Chinese and Russian competitors.
That's not to say that face recognition never had a problem dealing with the faces of women and minorities. A decade ago, when the technology was younger, it was often less accurate in identifying minorities and women.
Two agencies that I know well—the Transportation Security Administration and Customs and Border Protection (CBP)—depend heavily on identity-based screening of travelers. As they rolled out algorithmic face recognition, they reported on the results. And, like NIST, they found "significant improvements" in face recognition tools in just the two years between a 2017 pilot and the start of operations in 2019. Those improvements seriously undercut the narrative of race and gender bias in face recognition. While CBP doesn't collect data on travelers' race, it does know a lot about travelers' country of citizenship, which in turn is often highly correlated to race; using this proxy, CBP found that race had a "negligible" effect on the accuracy of its face matches. It did find some continuing performance differences based on age and gender, but those had declined a lot thanks to improvements in operational factors like illumination. These changes, the study found, "led to a substantial reduction in the initial gaps in matching for ages and genders": In fact, by 2019 the error rate for women was 0.2 percent, better than the rate for men and much better than the 1.7 percent error rate for women found in 2017.
In short, the evidence about bias in facial recognition evokes Peggy Lee's refrain: "Is that all there is?" Sadly, the answer is yes; that's all there is. For all the intense press and academic focus on the risk of bias in algorithmic face recognition, it turns out to be a tool that is very good and getting better, with errors attributable to race and gender that are small and getting smaller—and that can be rendered insignificant by the simple expedient of having people double check the machine's results by using their own eyes and asking a few questions.
One can hope that this means that the furor over face recognition bias will eventually fade. Unfortunately, the cost of that panic is already high. The efficiencies that face recognition algorithms make possible are being spurned by governments caught up in what amounts to a moral panic. A host of cities and at least five states (Maine, Vermont, Virginia, Massachusetts and New York) have adopted laws banning or restricting state agencies' use of face recognition.
Perhaps worse, tying the technology to accusations of racism has made the technology toxic for large, responsible technology companies, driving them out of the market. IBM has dropped its research entirely. Facebook has eliminated its most prominent use of face recognition. And Microsoft and Amazon have both suspended face recognition sales to law enforcement.
These departures have left the market mainly to Chinese and Russian companies. In fact, on a 2019 NIST test for one-to-one searches, Chinese and Russian companies scored higher than any Western competitors, occupying the top six positions. In December 2021, NIST again reported that Russian and Chinese companies dominated its rankings. The top-ranked U.S. company is Clearview AI, whose business practices have been widely sanctioned in Western countries.
Given the network effects in this business, the United States may have permanently ceded the face recognition market to companies it can't really trust. That's a heavy price to pay for indulging journalists and academics eager to prematurely impose a moral framework on a developing technology.