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Police Use of Facial Recognition Technology and Racial Bias – An Assessment of Criticisms of Its Current Use

Law enforcement has transformed drastically by advances in technology. Law enforcement bodies around the world have adopted facial recognition capabilities powered by artificial intelligence and contend that facial recognition technology is an effective tool in preventing, disrupting, investigating, and responding to crime. As the practice has grown, so have criticisms of its use and policing outcomes. Criticisms relate to the violation of civil liberties, namely the potential for abuse, propensity for inaccuracies, and improper use. In an effort to assess the validity of these criticisms, this paper examines the link between facial recognition technology and racial bias through an analysis of existing research and the use of a case study of an American municipality that has banned the use of facial recognition technology by police. Studies to date demonstrate a propensity for algorithms to mirror the biases of the datasets on which they are trained, including racial and gender biases; rates of match inaccuracy were consistently seen in relation to black persons, particularly black females. In addition to academic research, multiple examples of misidentifications of black citizens in the United States, along with related commentary from human rights and civil liberties groups, suggests that these concerns are translating into real world injustices. This paper validates concerns with the use of facial recognition technology for law enforcement purposes in the absence of adequate governance mechanisms.

Artificial Intelligence, Policing, Facial Recognition, Crime, Law Enforcement, Justice, Race

Seppy Pour. (2023). Police Use of Facial Recognition Technology and Racial Bias – An Assessment of Criticisms of Its Current Use. American Journal of Artificial Intelligence, 7(1), 17-23.

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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