The news is full of reports about where violent crimes have taken place, but we rarely hear about which neighborhoods are experiencing the most tax fraud or embezzlement. White Collar Crime Risk Zones aims to make those areas visible by predicting where financial crimes are most likely to occur across the U.S.
Predictive policing systems used by law enforcement rely on data about past crimes to help predict where future crimes may occur. These predictive systems are based on police data and may serve to reinforce the existing biases of police departments. White Collar Crime Risk Zones uses machine learning to invert those systems, to find out which neighborhoods are considered risky when you focus on financial crimes. The video you see here is a tour of zones in the U.S. The faces are composite images based on LinkedIn profiles of top executives in the area. What are the consequences of policing based on one data-set of crimes, while excluding another?
See more information or download the app at: https://whitecollar.thenewinquiry.com