The United States Department of Justice (DOJ) has recently come under intense scrutiny regarding its police grant program. A group of US lawmakers, alarmed by the lack of oversight and accountability, have raised concerns about the use of federal grants to purchase AI-based “policing” tools known to be inaccurate and prone to exacerbating biases within US police forces. In this article, we will delve into the flawed DOJ police grant program, the dangers of predictive policing tools, and the urgent need for a comprehensive investigation and reforms.
The lawmakers’ letter to the Department of Justice, which was first obtained by WIRED, reveals alarming information about the grant program. Contrary to expectations, the DOJ has failed to investigate whether the departments awarded grants have purchased discriminatory policing software. This revelation has only intensified concerns among Congress members, who argue that the DOJ must ensure grant recipients do not utilize these systems in ways that perpetuate discrimination and bias.
The Obligation to Review
Led by Senator Ron Wyden, the lawmakers remind the Department of Justice of its legal obligation to periodically review grant recipients’ compliance with Title VI of the Civil Rights Act. This legislation prohibits the funding of programs that exhibit racial, ethnic, or national origin discrimination, regardless of intent. The DOJ’s negligence in tracking the use of funds awarded under the Edward Byrne Memorial Justice Assistance Grant Program raises serious questions about its commitment to upholding civil rights and fairness within law enforcement.
Independent investigations conducted by the press reveal the inherent biases present in popular “predictive” policing tools. These systems, which rely on historical crime data, often perpetuate long-standing biases and disproportionately affect predominantly Black and Latino neighborhoods. The flawed algorithms not only fail to accurately predict crimes but also contribute to the over-policing and unjust surveillance of marginalized communities.
Dangerous Feedback Loops
The endemic flaws in predictive policing create a dangerous feedback loop that further exacerbates bias. By relying on distorted historical data that is influenced by falsified crime reports and disproportionate arrests of people of color, these systems legitimize unjustified stops and arrests in minority neighborhoods. As a result, biased statistics on crime locations emerge, perpetuating the overrepresentation of marginalized communities in law enforcement’s crosshairs.
A headline from The Markup succinctly captures the extent of the problem: “Predictive Policing Software Terrible At Predicting Crimes.” The publication’s recent analysis of 23,631 police crime predictions revealed an alarming accuracy rate of approximately 1 percent. This abysmal performance raises serious doubts about the validity and effectiveness of predictive policing tools, which are often hailed as technologically advanced solutions.
A Call for Action
In light of the alarming revelations surrounding the DOJ’s police grant program and the inherent biases present in predictive policing, the lawmakers’ letter concludes with a strong call for action. They urge the Department of Justice to halt all grants for predictive policing systems until a thorough investigation is conducted. The primary objective of this investigation should be to ensure that grant recipients commit to using these systems in a manner that does not perpetuate discrimination or disproportionately target marginalized communities.
Accountability and transparency are essential in combating systemic biases within law enforcement. The Department of Justice’s failure to adequately review the use of federal funds for discriminatory policing software raises significant concerns. Urgent action is needed to rectify the flaws in the police grant program and safeguard the civil rights of all citizens. By addressing the biases and limitations of predictive policing tools, law enforcement agencies can foster trust, promote fairness, and work towards creating a more equitable society.
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