White Collar Crime Risk Zones, predicting middle class criminality


Similar to what Philip Dick told us in 1956, in Minority Report, our ‘smart cities’ are now trying to become “safe cities”. There are a lot of IT companies already working with the various military forces, creating mapping systems that record and catalog criminal acts. And there are plenty of online platforms and apps that let you access your city map and, for example, check a certain neighbourhood before buying a house, or before moving there… More recently, software developed for law enforcement agencies implements so-called ‘predictive policing’, integrating external and internal data in order to identify trends in criminal activity, and in turn predict where such activity might occur in the future and deploy services accordingly. Although this software promises to radically change police activities, it faces rising criticisms for the use of historical training data, focusing only on street crimes and perpetuating prejudices against already damaged areas. “White Collar Crime Risk Zones” developed by Brian Clifton, Sam Lavigne and Francis Tseng for The New Inquiry Magazine is an artwork designed to trigger a strategic debate and to turn on the spotlights on the disparities and prejudices that the above mentioned models implement in contemporary society. Using data from the Financial Regulatory Authority from 1964 to present, the work uses a machine learning algorithm to predict where financial crimes in the United States are most likely to occur, shifting attention from street crimes to white-collar (middle class) crimes. It therefore identifies places with a risk of crimes such as unauthorised trading, or breach of fiduciary duty. In a navigable map, red areas will indicate high-risk zones, and yellow areas moderate risk zones. A practical App can then help users to move around the city safely, notifying the dangers of, for example, nearby ‘auction disturbances’ which should be avoided, and the potential density of such crimes in certain areas, with all the due consequences… Benedetta Sabatini


Sam Lavigne – White Collar Crime Risk Zones