Clifton, Brian, author.

White Collar Crime Risk Zones.

[Place of publication not identified] : The New Inquiry, 2017.

1 online resource

"Sitting somewhere between Minority Report and the OurRevolution PAC, White Collar Crime Risk Zones is a website and app that use machine learning to predict where financial crimes will happen across the U.S." The app's system was trained on financial malfeasance reports going back to 1964, and by referencing events with geotagged cartography it can predict financial crimes at the city-block-level with an accuracy of 90.12%. Typically, the logic of predictive policing is applied to 'street' level crime (drugs, assault, etc), and has rarely (if ever) been applied to the financial crimes of white collar criminals. Turning the technology on the bankers can't help but seem tongue-in-cheek - even though there's nothing funny about swindling people, nor anything evidently unserious about the software's efforts to map the potentiality of financial crime. Further, the app uses facial recognition software to give the user a readout of the most likely culprit, should they enter a geographic zone with high statistical potential: unsurprisingly, its always a generic white guy. While this reductio ad absurdum strategy reads as shrewd critique of social racism, it also recalls a long history of using photographic imaging to make false predictions and arrests, and points to the (many, obvious) darker uses of AI. This entry is included in Library Stack as part of the house collection Reality Winners."-- provided by distributor.

Biometric identification.
Biopolitics.
Data mining.
Electronic surveillance in art.
Finance.
Racism.
Identification biométrique.
Biopolitique.
Exploration de données (Informatique)
Surveillance électronique dans l'art.
Finances.
Racisme.
finance.

Software.
Tracts (Ephemera)
Pamphlets

Lavigne, Sam, author.
Tseng, Francis, author.
Library Stack, distributor.
Library Stack.