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Facial Recognition Technology in Law Enforcement: Regulations, Crime Clearance, and Bias
Jeongmin Kim, Heejin Joo
The widespread use of Facial Recognition Technology (FRT) in law enforcement has triggered ethical, legal, and societal debates, particularly regarding its effectiveness and potential biases. In response, various jurisdictions have implemented regulations. This study examines the impact of full FRT bans on crime clearance rates, focusing on differences across races and genders. The results show a significant decrease in clearance rates following FRT bans, with uniform effects across all demographic groups. These findings challenge assumptions about technological bias and contribute to discussions on technology regulation and AI bias, offering insights for researchers and policymakers.

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