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San Francisco, sitting just north of Silicon Valley, may be one of the most technology-fluent places in the country, yet on May 14th, the city’s Board of Supervisors led by Aaron Peskin voted in an 8-1 decision to ban the use of facial recognition technology. (sfchronicle.com) The council will need to vote a second time before it goes to the mayor’s desk to be signed into law. Facial recognition refers to the ability of computers to identify people almost instantaneously in pictures or on video by comparing their facial image to a database of pictures such as driver’s license photos or criminal records. The ban on facial recognition covers all city government, including the police, but it does not extend to the private sector or the port and airport that fall under Federal Government control.
Needless to say, the San Francisco police who currently do not use facial recognition will not have access to a tool that many towns and cities have adopted in their efforts to battle crime. Authors Gregory Barber and Tom Simonite cite research conducted by the Georgetown Center on Privacy and Technology in an article titled “Some Cities are Moving into Real-time Facial Surveillance,” that concludes that the cities of Detroit and Chicago have already purchased real-time facial recognition software. (wired.com) However, at the time of the article, the police departments from Detroit and Chicago claim not to have turned on the real-time facial recognition capability yet. Federal, state, and local law enforcement agencies have used facial recognition on static images to identify suspects from surveillance footage. Major social media such as Facebook use facial recognition on photos uploaded by users all the time to identify people all the time. It remains unclear whether or not the ban in San Francisco would also apply to static image analysis.
The San Francisco ban on facial recognition symbolizes a vital move to put the genuine possibility of total surveillance of the populous up for debate. Privacy advocates point out that the technology of facial recognition suffers from bias. Studies have shown that facial recognition systems such as those developed by Amazon and IBM detect gender and race most accurately for white males and perform less accurately for people of color and women. Such bias runs the risk of false accusations and arrests happening to innocent people. Such a consideration will diminish over time, given the continuous improvement of the technology as well as the proliferation of inexpensive high definition cameras. The more significant issue relates to the possibility of a society living under constant monitoring and what it will do to a free society. The American Civil Liberties Union (ACLU) declares, “Unlike many other biometric systems, facial recognition can be used for general surveillance in combination with public video cameras, and it can be used in a passive way that doesn’t require the knowledge, consent, or participation of the subject. The biggest danger is that this technology will be used for general, suspicionless surveillance systems.” (aclu.org)
On the 14th of May 19, 2019, the San Francisco Board of Supervisors voted yes to a law that would ban the use of facial recognition technology by the city government, including the police. The bill still needs the signature of the Mayor to get enacted, but the sentiment should lay the path for more cities and states to consider the danger of total surveillance on a free society. Home surveillance applications video enabled door bells, for example, provide a sense of safety, but they also bring in a heightened sense of paranoia and impending danger as noted in “Neighborhood Security Apps Are Making Us Wildly Paranoid.” (thefuture.com) We should carefully follow how San Francisco deals facial recognition technology and ask ourselves how much freedom we would forfeit for better safety and law enforcement.
Dr. Smith’s career in scientific and information research spans the areas of bioinformatics, artificial intelligence, toxicology, and chemistry. He has published a number of peer-reviewed scientific papers. He has worked over the past seventeen years developing advanced analytics, machine learning, and knowledge management tools to enable research and support high level decision making. Tim completed his Ph.D. in Toxicology at Cornell University and a Bachelor of Science in chemistry from the University of Washington.
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