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Additionally, face recognition can potentially target other marginalized populations, such as undocumented immigrants by ICE, or Muslim citizens by the NYPD.ĭiscriminatory law enforcement practices were highlighted following the murder of George Floyd by the Minneapolis PD. Advocates fear that even if face recognition algorithms are made equitable, the technologies could be applied with the same spirit, disproportionately harming the Black community in line with existing racist patterns of law enforcement. In 18 th century New York, “lantern laws” required enslaved people to carry lanterns after dark to be publicly visible. Face recognition in racial discrimination by law enforcementĪnother key source of racial discrimination in face recognition lies in its utilization. Companies that provide these services have a responsibility to ensure that they are equitable – both in their technologies and in their applications. As Amazon has marketed its technology to law enforcement, these discrepancies are concerning. However, Amazon’s responses were defensive, alleging issues with auditors’ methodology rather than addressing racial bias. This result corroborated an earlier assessment of Rekognition’s face-matching capability by the American Civil Liberties Union (ACLU), in which 28 members of Congress, disproportionately people of color, were incorrectly matched with mugshot images. A Gender Shades re-audit confirmed a decrease in error rates on Black females and investigated more algorithms including Amazon’s Rekognition, which also showed racial bias against darker-skinned women (31% error in gender classification). IBM and Microsoft announced steps to reduce bias by modifying testing cohorts and improving data collection on specific demographics. These compelling results have prompted immediate responses, shaping an ongoing discourse around equity in face recognition.

These algorithms consistently demonstrated the poorest accuracy for darker-skinned females and the highest for lighter-skinned males. The Gender Shades project revealed discrepancies in the classification accuracy of face recognition technologies for different skin tones and sexes. Independent assessment by the National Institute of Standards and Technology (NIST) has confirmed these studies, finding that face recognition technologies across 189 algorithms are least accurate on women of color.įigure 1: Auditing five face recognition technologies. All three algorithms performed the worst on darker-skinned females, with error rates up to 34% higher than for lighter-skinned males (Figure 1). Subjects were grouped into four categories: darker-skinned females, darker-skinned males, lighter-skinned females, and lighter-skinned males.

In the landmark 2018 “Gender Shades” project, an intersectional approach was applied to appraise three gender classification algorithms, including those developed by IBM and Microsoft. A growing body of research exposes divergent error rates across demographic groups, with the poorest accuracy consistently found in subjects who are female, Black, and 18-30 years old. Inequity in face recognition algorithmsįace recognition algorithms boast high classification accuracy (over 90%), but these outcomes are not universal. Even if accurate, face recognition empowers a law enforcement system with a long history of racist and anti-activist surveillance and can widen pre-existing inequalities. More disturbingly, however, the current implementation of these technologies involves significant racial bias, particularly against Black Americans. This participation occurs without consent, or even awareness, and is bolstered by a lack of legislative oversight.

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Police use face recognition to compare suspects’ photos to mugshots and driver’s license images it is estimated that almost half of American adults – over 117 million people, as of 2016 – have photos within a facial recognition network used by law enforcement. Why? Of the dominant biometrics in use (fingerprint, iris, palm, voice, and face), face recognition is the least accurate and is rife with privacy concerns. Despite widespread adoption, face recognition was recently banned for use by police and local agencies in several cities, including Boston and San Francisco. It is employed for law enforcement surveillance, airport passenger screening, and employment and housing decisions. But face recognition, the technology behind these features, is more than just a gimmick. We unlock our iPhones with a glance and wonder how Facebook knew to tag us in that photo.

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