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Can technology-driven crime prevention coexist with fairness and justice?
Predictive Justice: A Double-Edged Sword in Crime Prevention
Exploring the Promise and Pitfalls of Predictive Policing in Modern Law Enforcement
Recently, Predictive policing has gained some attention in various parts of the world, including the United States. This relatively novel strategy of fighting crime employs computer algorithms to predict where the crimes are likely to happen. Due to its controversial nature, I am going to talk about predictive policing, which has sparked significant debate. While this strategy promises more efficient law enforcement, however, we can not overlook the ethical snags it presents. Understanding the crux of these issues would help us understand why cities like Los Angeles, New York, and Chicago have tested this strategy and decided to abandon this approach.
Disproportionate Targeting
One of the significant drawbacks regarding the use of predictive policing is its tendency to target low-income neighborhoods and high-minority areas disproportionately. This biased trend often leads to increased police presence in these communities, inducing systemic racism and violating principles of fair justice.
We can only imagine living in a neighborhood where police patrols are a constant reminder that there are high crimes among us when it may be false. This creates an atmosphere of tension and distrust rather than safety and security. So, the question remains:
Is it justifiable to trade fairness for prevention?
Biased Data
In addition to disproportionate targeting, the accuracy of predictive models has also come under scrutiny. Merely because the essence of predictive policing hinges on the quality and diversity of the data they are trained on. Therefore, if the historical data is biased, the algorithms will too reflect those biases, leading to unfair targeting of certain groups. This raises another crucial question:
Can we rely on a system that potentially inherits and amplifies the flaws of past policing practices?
