{"id":266370,"date":"2020-08-25T05:00:52","date_gmt":"2020-08-25T09:00:52","guid":{"rendered":"https:\/\/canadianinquirer.net\/v1\/?p=266370"},"modified":"2020-08-25T10:06:14","modified_gmt":"2020-08-25T14:06:14","slug":"ai-technologies-like-police-facial-recognition-discriminate-against-people-of-colour","status":"publish","type":"post","link":"https:\/\/canadianinquirer.net\/v1\/2020\/08\/25\/ai-technologies-like-police-facial-recognition-discriminate-against-people-of-colour\/","title":{"rendered":"AI technologies \u2014 like police facial recognition \u2014 discriminate against people of colour"},"content":{"rendered":"
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Experts suggest that Williams is not alone, and that others have been subjected to similar injustices. The ongoing controversy about police use of Clearview AI certainly underscores the privacy risks posed by facial recognition technology. But it\u2019s important to realize that not all of us bear those risks equally. (Pixabay photo)<\/figcaption><\/figure>\n

Detroit police wrongfully arrested Robert Julian-Borchak Williams in January 2020 for a shoplifting incident that had taken place two years earlier<\/a>. Even though Williams had nothing to do with the incident, facial recognition technology used by Michigan State Police \u201cmatched\u201d his face with a grainy image obtained from an in-store surveillance video showing another African American man taking US$3,800 worth of watches.<\/p>\n

Two weeks later, the case was dismissed at the prosecution\u2019s request. However, relying on the faulty match, police had already handcuffed and arrested Williams in front of his family, forced him to provide a mug shot, fingerprints and a sample of his DNA, interrogated him and imprisoned him overnight.<\/p>\n

Experts suggest that Williams is not alone, and that others have been subjected to similar injustices. The ongoing controversy about police use of Clearview AI certainly underscores the privacy risks posed by facial recognition technology. But it\u2019s important to realize that not all of us bear those risks equally<\/a>.<\/p>\n

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Training racist algorithms<\/h2>\n

Facial recognition technology that is trained on and tuned to Caucasian faces<\/a> systematically misidentifies and mislabels racialized individuals: numerous studies report that facial recognition technology is \u201cflawed and biased, with significantly higher error rates when used against people of colour<\/a>.\u201d<\/p>\n

This undermines the individuality and humanity of racialized persons<\/a> who are more likely to be misidentified as criminal. The technology \u2014 and the identification errors it makes \u2014 reflects and further entrenches long-standing social divisions that are deeply entangled with racism, sexism, homophobia, settler-colonialism and other intersecting oppressions.<\/p>\n