4.6: Racial Profiling
Racial profiling is stereotyping. In short, racial profiling is the act of suspecting criminal activity based solely on the color of a person’s skin or their minority status, or their ethnic origin (Warren, & Farrell, 2009).
Definitive! Racial Profiling
According to the American Civil Liberties Union (ACLU),
“ Racial profiling refers to the practice by law enforcement officials of targeting individuals for suspicion of crime based on the individual's race, ethnicity, religion, or national origin. Criminal profiling, generally, as practiced by police, is the reliance on a group of characteristics they believe to be associated with crime. Examples of racial profiling are the use of race to determine which drivers to stop for minor traffic violations (commonly referred to as ‘driving while black or brown’), or the use of race to determine which pedestrians to search for illegal contraband.”
While racial profiling has been reduced in recent years, new advances in technology have contributed to the concern of potential racial profiling in face recognition technology.
Modern Face Recognition Technology
We unlock our iPhones with a glance and wonder how Facebook knew to tag us in that photo. But face recognition, the technology behind these features, is more than just a gimmick. It is employed for law enforcement surveillance, airport passenger screening, and employment and housing decisions. Despite widespread adoption, face recognition was recently banned for use by police and local agencies in several cities, including Boston and San Francisco. 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.
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. This participation occurs without consent, or even awareness, and is bolstered by a lack of legislative oversight. More disturbingly, however, the current implementation of these technologies involves significant racial bias, particularly against Black Americans. 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.
Inequity in Face Recognition Algorithms
Face recognition algorithms boast high classification accuracy (over 90%), but these outcomes are not universal. 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. 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. Subjects were grouped into four categories: darker-skinned females, darker-skinned males, lighter-skinned females, and lighter-skinned males. All three algorithms performed the worst on darker-skinned females, with error rates up to 34% higher than for lighter-skinned males (Figure 1). 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.
Figure 4.1. Auditing five face recognition technologies. The Gender Shades project revealed discrepancies in the classification accuracy of face recognition technologies for different skin tones and sexes. These algorithms consistently demonstrated the poorest accuracy for darker-skinned females and the highest for lighter-skinned males. 2
These compelling results have prompted immediate responses, shaping an ongoing discourse around equity in face recognition. IBM and Microsoft announced steps to reduce bias by modifying testing cohorts and improving data collection on specific demographics. 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). 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. However, Amazon’s responses were defensive, alleging issues with auditors’ methodology rather than addressing racial bias. As Amazon has marketed its technology to law enforcement, these discrepancies are concerning. Companies that provide these services have a responsibility to ensure that they are equitable – both in their technologies and in their applications.