It's time to shut mouths. You racosts don't do the research necessary to be arguing. This is from MIT.
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"There's a long history of data being weaponized against Black communities."
Inequality and the misuses of police power don’t just play out on the streets or during school riots. For Milner and other activists, the focus is now on where there is most potential for long-lasting damage: predictive policing tools and the abuse of data by police forces. A number of studies have shown that these tools perpetuate systemic racism, and yet we still know very little about how they work, who is using them, and for what purpose. All of this needs to change before a proper reckoning can take place. Luckily, the tide may be turning.
There are two broad types of predictive policing tool. Location-based algorithms draw on links between places, events, and historical crime rates to predict where and when crimes are more likely to happen—for example, in certain weather conditions or at large sporting events. The tools identify hot spots, and the police plan patrols around these tip-offs. One of the most common, called PredPol, which is used by dozens of cities in the US, breaks locations up into 500-by-500 foot blocks, and updates its predictions throughout the day—a kind of crime weather forecast.
Other tools draw on data about people, such as their age, gender, marital status, history of substance abuse, and criminal record, to predict who has a high chance of being involved in future criminal activity. These person-based tools can be used either by police, to intervene before a crime takes place, or by courts, to determine during pretrial hearings or sentencing whether someone who has been arrested is likely to reoffend. For example, a tool called COMPAS, used in many jurisdictions to help make decisions about pretrial release and sentencing, issues a statistical score between 1 and 10 to quantify how likely a person is to be rearrested if released.
The problem lies with the data the algorithms feed upon. For one thing, predictive algorithms are easily skewed by arrest rates. According to US Department of Justice figures, you are more than twice as likely to be arrested if you are Black than if you are white. A Black person is five times as likely to be stopped without just cause as a white person.
Lack of transparency and biased training data mean these tools are not fit for purpose. If we can’t fix them, we should ditch them.
www.technologyreview.com
So first off the data from police is not correct and racism plays a role in data compilation.
For example whites led in murder arrests until the mid 1980's. Coincidentally when Reagan/Bush 1 allowed crack into the black community to pay for American covert military operations, suddwnly blacks had more murders than whites.