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ITIF calls for constructive conversation on facial recognition in response to privacy groups’ Amazon letter

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The Information Technology & Innovation Foundation (ITIF) has responded to an open letter sent by privacy advocacy groups to Amazon, calling on the cloud giant to stop marketing its facial recognition products to law enforcement, saying the groups are attempting to demonize the technology.

The goal of rules or norms governing the use of facial recognition technology should be to protect people from the potential harms of its misuse, not to completely stop its use, ITIF says in a blog post by Research Analyst Alan McQuinn.

“This week’s letter cites concerns of over-policing and surveillance, especially in communities of color, as the reason to not sell this technology to the government. However, the widespread deployment of body cameras can help reduce this concern. Body cameras can help protect communities of color by holding law enforcement accountable. Indeed, indisputable video evidence can offer a better record for an altercation, reducing the uncertainty over events and helping bring about justice for all involved. This is why even the ACLU calls on-body police cameras a “win for all” as long as the right protections are in place. There is no reason to suggest the same cannot be true for facial recognition technology.”

ITIF calls for a constructive conversation about how to tailor regulations, rather than alarmist rhetoric, and points out that if the privacy groups are successful in making Amazon stop marketing its technology to law enforcement, another company can simply take their place to meet the market demand.

“Working with the government and making government functions more efficient and effective should be seen as patriotic,” McQuinn writes. “Only by de-stigmatizing the technology can we have a constructive conversation and adequately capture its benefits.”

YITU AI Research Scientist Dr. Wu Shuang expressed similar sentiments to Biometric Update in a recent interview on the rapid advances in AI.

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