The use of facial recognition technologies by the Chicago Police Department (CPD) has sparked many controversies over its ethicality and efficacy in fighting crime. Main arguments in the debate of whether to use facial recognition technology in policy come from data privacy issues and flaws in existing technologies to correctly identify faces. This thesis contributes to the discussion by bringing tangible data insights from historical Chicago crime data, specifically after the implementation of DataWorks Plus’s facial recognition technologies in 2013. A negative correlation between motor vehicle theft and use of facial recognition technology was found, however no correlation was found for the other types of crimes examined: arson, assault, criminal damages, homicide, robbery, sex offense, theft. Based on the lack of impact on violent or private crimes, and valid concerns over algorithmic bias, this thesis recommends limiting the police use of facial recognition software to non-violent crimes and reviewing city data retention policies for video surveillance footage.