First Published on 19 August 2023.

Introduction

As technology continues to advance, many aspects of our lives have become more convenient and efficient, from personalized streaming recommendations to targeted social media ads. One area where technology has gained prominence is in policing through the use of algorithms for predictive policing. However, this seemingly futuristic approach to law enforcement raises critical concerns, particularly when it comes to bias, inequality, and the perpetuation of existing issues within the criminal justice system.

The Promise and Peril of Predictive Policing

Predictive policing is the application of algorithms to predict and prevent future crimes. While proponents argue that it can optimize resource allocation and improve crime prevention, the reality is more complex. Predictive policing relies heavily on historical crime data, which often stems from traditional policing methods that have been shown to be unequal and unjust, particularly in the United States.

The application of policing in the United States has been marred by unequal treatment and systemic biases. Black Americans are disproportionately targeted by law enforcement, leading to racial profiling, unfair stops, and higher rates of incarceration. These deep-rooted issues can be exacerbated by predictive policing algorithms, which learn from historical data that may carry these biases. Consequently, rather than rectifying the problem, predictive policing algorithms risk perpetuating the very inequalities they aim to address.

Next, algorithms are only as objective as the data they are trained on and the instructions they receive. Crime data, which is the foundation of predictive policing algorithms, is sourced from police records such as 911 calls and incident reports. However, these records are not immune to biases, manipulation, or distortion. Recent cases of police departments manipulating crime data to present a false narrative raise serious concerns about the integrity of the information feeding into these algorithms.

Lastly, transparency is a crucial component of responsible algorithmic deployment. Unfortunately, many police departments lack transparency when it comes to their predictive policing algorithms. Limited information about the algorithms’ functioning and the data they use makes it challenging to assess their accuracy and potential biases. The lack of transparency undermines public trust and can perpetuate unchecked use of these algorithms.

Addressing the Problem: A Path Forward

1. Comprehensive Data Collection: To ensure the accuracy and fairness of predictive policing algorithms, it’s essential to collect comprehensive and unbiased data. This includes revisiting traditional policing practices, eliminating racial profiling, and improving data integrity.
2. Algorithmic Accountability: Law enforcement agencies must take responsibility for the algorithms they deploy. This involves regular audits and assessments of the algorithms to identify and rectify biases and inaccuracies.
3. Transparency and Oversight: Establishing regulations that mandate transparency in the use of predictive policing algorithms can help build public trust. Independent oversight and audits can ensure that these algorithms are not perpetuating biases.
4. Diverse Input and Collaboration: Developing predictive policing algorithms should involve collaboration with diverse stakeholders, including communities that have historically been disproportionately affected by biased policing. Diverse input can lead to fairer algorithms that serve everyone’s interests.
5. Constant Evaluation: Police departments must continuously evaluate the effectiveness of predictive policing algorithms. If these technologies do not show a clear reduction in crime rates while perpetuating biases, their continued use should be reconsidered.