The big problem
Predictive crime data could help eradicate racial profiling in policing if the data were clean of racial prejudice. Unfortunately, that data is generated based on systemic police practices that have marginalized ethnic minorities in this country for decades.
Systems that rely on historical crime data, by their nature, will give results reflective of traditional police practice: The biases in the data are baked into the human practices that generated the data in the first place. When it comes to drug crime, for example, a higher proportion of white Americans have used drugs compared to African-Americans, but African-Americans — largely because of traditional police practices — are the ones incarcerated at disproportionately high rates.
“The academic community suggests that crime, including serious violent crime, is reported to the police about 50% of the time,” Christopher Herrmann, a professor at the John Jay School of Criminal Justice, told Mic. “That means, at best, these predictive software programs are beginning their predictions with only half of the picture.”
Many of the crimes that go unreported are crimes where the victim doesn’t feel like the police will adequately support them, like sexual assault and hate crimes. Diverting police resources toward hotspots identified by these maps means more attention is paid to types of crime already well-covered by police officers.
Cathy O’Neil is a mathematician whose upcoming book, Weapons of Math Destruction, explores how big data can amplify prejudice. O’Neil says that algorithmic models can only amplify and expose insights based on the human behavior that created the data in the first place.