The Flawed Nature of AI-Based Policing Tools according to US Lawmakers

The United States Department of Justice (DOJ) has recently come under scrutiny regarding its grant program for state and local police agencies. Concerns have been raised by a group of US lawmakers that the DOJ is awarding federal grants to purchase AI-based “policing” tools that are known to be inaccurate and prone to exacerbating biases within the US police forces. This article delves into the lawmakers’ objections, the DOJ’s response, and the implications of using such predictive policing systems.

In a letter obtained by WIRED, seven members of Congress expressed their dissatisfaction with the DOJ’s handling of the police grant program. Despite requesting information about the purchase of discriminatory policing software, the lawmakers felt that the agency’s responses only served to heighten their concerns. They argue that the DOJ should halt all grants for predictive policing systems until it can ensure that recipients will not use these tools in a discriminatory manner.

The DOJ’s lack of oversight becomes apparent when it acknowledged that it had not been keeping track of whether police departments were utilizing the funding from the Edward Byrne Memorial Justice Assistance Grant Program to acquire predictive policing tools. This failure to properly monitor grant recipients goes against the DOJ’s obligation to periodically review compliance with Title VI of the Civil Rights Act, which prohibits funding programs that discriminate based on race, ethnicity, or national origin, regardless of intent.

Independent investigations have revealed that predictive policing tools, trained on historical crime data, tend to replicate existing biases within law enforcement. These tools, which are often seen as having a scientific legitimacy, have been found to be inaccurate and perpetuate the over-policing of predominantly Black and Latino neighborhoods. The flawed nature of these predictive systems is underscored by The Markup’s headline, “Predictive Policing Software Terrible At Predicting Crimes,” which highlights the low accuracy rate in crime predictions.

Senator Ron Wyden and other lawmakers argue that the use of biased predictions in predictive policing creates dangerous feedback loops. These systems rely on historical data that may be distorted by falsified crime reports and disproportionate arrests of people of color. As a result, the predictions themselves become biased, leading to further disproportionate stops and arrests in minority neighborhoods. This perpetuates a cycle of bias and over-policing that undermines the principles of fairness and justice.

The concerns raised by US lawmakers regarding AI-based policing tools expose the flaws within the DOJ’s grant program and its failure to address issues of discrimination and bias. The reliance on predictive policing systems that replicate existing biases only serves to perpetuate injustice within our communities. It is imperative that the DOJ takes immediate action to ensure that grant recipients do not use these systems in a discriminatory manner. Ultimately, a fair and just society can only be achieved when the tools used by law enforcement are free from bias and accurately reflect the diverse communities they serve.

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