Artificial intelligence (AI) has been shaping up to be a key player in modern law enforcement. With its development, machine learning and the ability to analyze vast amounts of data have become a staple in modern-day policing and security efforts worldwide. Law enforcement officials are steadily implementing AI tech due to its potential to mitigate crime and enhance public safety. But along with progress come the potential risks and rewards, and AI in law enforcement is not without ethical and criminal ramifications.
This article aims to explore the potential pros and cons of using AI in law enforcement while looking at different perspectives and real-world examples. We will also be looking at how AI in law enforcement can benefit society while simultaneously addressing potential risks.
**What is AI in Law Enforcement?**
AI is not a new concept in law enforcement. Law enforcement agencies have been using AI-supported tools in investigations for several years. Comparable to a robotic assistant of sorts, the technology is assisting law enforcement to complete tasks that are too repetitive and personnel-intensive for human analysts. Some of the AI systems currently utilized by law enforcement are facial recognition software, predictive policing software, and even automated drones and robots.
Facial recognition tech is being used across the globe to identify people present in surveillance footage and cross-reference it with a database of known offenders or persons of interest. Real-time facial recognition allows police forces to locate and apprehend fugitives rapidly. For example, the tech was used in China to identify and arrest a 31-year-old fugitive among seventy-thousand football fans in a stadium within 7 minutes. Amid the pandemic, Dubai police used AI-based facial recognition and other tech infrastructure to ensure the safety of UAE residents and, rapidly, identify those breaching Covid-19 restrictions.
**Potential Risks of AI in Law Enforcement**
As much as a boon it may seem, AI technology, by nature, poses some societal and ethical risks. Below, we highlight a few of the primary concerns when it comes to using AI in law enforcement.
**Error and Bias**
One of the significant risks of AI in law enforcement is the likelihood of inherent biases within the technological advancements. The AI algorithms tend to learn based on patterns, forced to it similarly by human operators. Still, human biases and prejudices that could be unknowingly encouraged in the tech are evident. For example, facial recognition software has faced significant backlash because of its inaccuracy in differentiating people of color, leading to erroneous arrests and damage to the reputation of innocent individuals.
**Hacking and Misuse**
Like any technology that is implemented on a global scale, AI’s potential for misuse is enormous. IT analysts highlight that the data collected from facial recognition software is vulnerable to be hacked, manipulated, and misused, leading to significant complications. The technology could be used to track people and illegally retain their data by malicious actors. Additionally, the same data could also be used to extort people or in other criminal activities.
**Lack of Accountability**
Another significant risk of AI in law enforcement is the lack of accountability. It is much easier to point blame on a machine instead of a human operator, who could control the technology, subject to the rules, regulations, and professional codes of conduct. AI has slowly evolved to become autonomous; however, it still requires oversight and validation from humans to limit errors. Recently, San Francisco became the first American city to restrict the use of facial recognition technology by law enforcement and other entities to maintain some level of accountability.
**Potential Benefits of AI in Law Enforcement**
**Improved Efficiency**
An unrelenting benefit of AI in law enforcement is improved efficiency and speed. As machine learning algorithms continue to evolve and improve over time, they prove to be more accurate and efficient than humans, thereby making essential decisions quicker. This includes investigations involving digital evidence, cybercrime investigations, and crime-scene investigations, among other things that should limit police resources and boost productivity.
**Better Public Safety**
The inclusion of Artificial Intelligence in law enforcement could provide police forces with a better grasp of public safety. Predictive policing software analyzes data obtained from past crime trends and activities to make informed decisions about necessary measures to prevent crime. The technology optimizes both the resources and simple processing associated with policing and ultimately enhances the public’s safety. London’s Metropolitan Police produced a model called the ‘PredPol’ (short for predictive policing) which identifies potential offenders and hotspots for crime and where they can focus their resources efficiently.
**Objectivity**
Another significant benefit of AI towards law enforcement is its impartiality – the machine learning process is commonly based on data rather than subjectivity. As a result, the outcomes are more objective and less vulnerable to unconscious biases that humans might have. For instance, instead of relying solely on a detective’s prejudices, surveillance cameras in a neighborhood could be programmed to detect probable criminals based on behavioral patterns.
**Conclusion**
There is no question that advancements in Artificial Intelligence technologies have vast potential to improve law enforcement efficiency and assist with crime-fighting. Still, it also poses significant ethical and societal risks when not executed appropriately. Nevertheless, when used correctly, AI technologies will prove to be revolutionary for law enforcement. It would make no sense on either side to foster a relationship where AI could eliminate jobs or responsibilities of human operators. Thus, AI should be used in low-Risk applications, assisting personnel in dull, mundane tasks, leaving officers to focus on the more critical activities. Lastly, decisions regarding AI should be made with extreme subjectivity, ethical scrutiny, and secure processes to ensure appropriate transparency and accountability.