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As the use of artificial intelligence is increasingly adopted across various sectors, it is poised to significantly transform law enforcement strategies across the U.S. This evolution is particularly evident in two emerging methods that leverage algorithms to process vast datasets, aiming to predict and preempt potential criminal activities. The first method, known as place-based predictive policing, employs a range of technologies to sift through crime data, thereby identifying potential “hotspots” where crimes are more likely to occur. The second method, termed person-based predictive policing, adopts a technology-centric strategy to anticipate potential criminal acts by identifying individuals or groups conceivably at a higher risk of engaging in criminal behavior.

Place-Based Predictive Policing

Place-based predictive policing harnesses the power of AI to run sophisticated algorithms that examine large volumes of data, including historical crime records. Notably, these computational procedures can reveal patterns and trends that might elude human analysts. In turn, this assessment can result in the projection of potential future crime locations and timings, enabling proactive police deployment.

A key technique in this approach is risk terrain modeling, which forecasts potential crime areas by analyzing crime statistics alongside environmental factors. This approach extends to geospatial policing strategies. Notably, the National Institute of Justice’s research revealed that an advanced form of this model, known as conjunctive analysis, successfully predicted regions with a heightened risk of future crimes in five cities. This method’s effectiveness lies in its ability to identify and consider various environmental factors that influence crime, thereby accurately pinpointing potential hotspots for criminal activity.

Near-repeat modeling is another method utilized in place-based predictive policing. It is centered around the concept that certain crimes, especially residential burglaries, are prone to recur near their original locations soon after the first event. This method operates on the understanding that perpetrators often target areas where they have previously succeeded, considering these spots as low-risk yet yielding high returns. It is particularly effective in predicting and preventing recurrent offenses, such as domestic violence or gang-related activities, which tend to happen in close spatial and temporal sequences. By meticulously analyzing past crime patterns, law enforcement agencies can foresee potential future crime scenes and times, thereby strategically deploying resources to avert these anticipated crimes.

Beyond these specific models, place-based predictive policing also employs broader data-driven strategies. It utilizes historical crime statistics, emergency call records, and socioeconomic information to develop various predictive models like regression, classification, and clustering. This holistic analysis deepens the understanding of crime dynamics within a jurisdiction.

Overall, as AI mapping software and electronic records management technologies become more sophisticated and user-friendly, their adoption in policing practices is likely to increase, making place-based predictive policing a more integral part of law enforcement strategy.

Person-Based Predictive Policing

Like place-based predictive policing, person-based predictive policing leverages advanced algorithms and large datasets to anticipate potential criminal acts. This approach focuses on analyzing various risk factors such as past arrests, victimization patterns, and other personal data to identify individuals or groups at a higher risk of engaging in criminal behavior.

This AI-powered analysis helps identify correlations and patterns that might not be immediately apparent to human analysts. For example, predictive policing systems have been employed by law enforcement agencies to predict risk and monitor individuals based on their past interactions with the law. These systems then use the data to formulate recommendations, some of which result in an increase in police visits and checks on targeted individuals and their families.

Another area of application for person-based predictive policing is in identifying individuals who might become victims of crimes. This method seeks to analyze patterns of past victimization and other relevant data to predict and prevent future occurrences of victimization. For example, between 2010 and 2020, the Chicago Police Department developed a comprehensive database designed to utilize analytics for identifying individuals most likely to be involved in shooting incidents, either as perpetrators or victims. This so-called ‘heat list’ or ‘strategic subjects list’ was also used to target gang members and their associates through information gathering, analysis, and social network mapping.

However, as law enforcement agencies begin to experiment with person-based risk models, there are growing concerns regarding the accuracy of risk scores and tiers, the potential for improperly trained personnel, and insufficient controls over who can access these systems internally and externally. To address these issues, there is an evident need to refine and rigorously evaluate these methods. This improvement process will likely involve implementing more comprehensive research techniques, such as randomized controlled trials. These trials can provide a more reliable assessment of the effectiveness of different predictive policing approaches, ensuring that they are both effective and responsibly implemented.

Legal Complexities

AI-powered predictive policing introduces several legal complexities, chief among them being privacy concerns. The use of personal data from the internet, social media, and CCTV footage by predictive policing systems raises alarming possibilities of misuse or data leakage. The inherent risks of storing such sensitive data are compounded by potential gaps in the skills or resources needed to ensure its security.

Furthermore, the widespread sharing of personal information on social media, often not perceived as public by users, enhances state surveillance capabilities significantly. Alarmingly, current laws fall short in restricting law enforcement’s use of social media intelligence. This legal gap becomes increasingly problematic as more personal data is shared online without clear privacy norms. Additionally, employing personal data to predict criminal behavior challenges the foundational legal principle of presumed innocence, effectively treating individuals as suspects before any proven wrongdoing.

Another major concern regarding predictive policing is the lack of transparency. Many law enforcement agencies employ these systems without sufficient public disclosure or input. There is often little information available about the data being used, the design of the algorithms, and how the predictions will be implemented in practice. This lack of transparency makes it difficult to conduct a meaningful public debate or to assess the systems’ fairness and accuracy. Advocacy groups like the ACLU have stressed the need for transparency, rigorous independent evaluation, and continuous assessment of these systems’ statistical validity and operational impact. They also highlight the potential for these systems to intensify enforcement in communities that already face disproportionate law enforcement scrutiny, thus exacerbating issues of racial and social inequality.

This is because historical crime data, which forms the basis of many predictive policing algorithms, is often tainted with years of racial bias. This can lead to over-policing in areas with a high concentration of racial and ethnic minorities, further entrenching stereotypes and social inequalities. Additionally, the accuracy of predictive policing is questionable, as crime data is often incomplete or inaccurate. This can lead to misleading conclusions about crime patterns and hotspots.

In response to these concerns, some jurisdictions have enacted strict regulations or even bans on the use of predictive policing tools. These measures reflect a growing awareness and caution about the legal implications of employing AI and big data in law enforcement, emphasizing the need to balance public safety with respect for civil liberties and privacy rights.

The Future of AI and Predictive Policing

The future of AI and predictive policing in law enforcement is a subject of increasing importance, marked by the necessity for comprehensive strategies to ensure the ethical integration and application of these technologies. The development and use of AI in policing contexts, particularly predictive policing, present a range of challenges and opportunities that law enforcement agencies must navigate.

To address these challenges, law enforcement agencies have been encouraged to develop comprehensive strategies for AI integration. This includes establishing governance structures for ethical and responsible AI use. Creating such structures involves defining clear AI policies, ensuring transparency in AI operations, and maintaining accountability for AI-driven decisions. Conducting rigorous AI risk evaluations is also essential to understand, measure, and manage potential risks to individuals and the broader community.

Conclusion

In summary, the integration of artificial intelligence into law enforcement, manifesting in the form of place-based and person-based predictive policing, heralds a transformative era in crime prevention and public safety management. Place-based predictive policing, with its focus on analyzing extensive crime data to anticipate crime hotspots, is evolving with advancements in data analytics and mapping technologies. Meanwhile, person-based predictive policing is emerging as a strategic tool to identify individuals or groups potentially at higher risk of criminal involvement, albeit not without its legal and ethical complexities.

Both methodologies, while promising in enhancing proactive policing, raise critical questions about privacy, data security, and the potential reinforcement of existing biases. The utilization of personal data, particularly from online sources, introduces legal challenges that necessitate a reevaluation of privacy norms and law enforcement practices. Moreover, the lack of transparency and potential for racial and social profiling in predictive policing algorithms underscores the need for stringent regulations, ethical guidelines, and public accountability.

As law enforcement agencies increasingly adopt these AI-driven tools, it is imperative to strike a balance between leveraging technology for public safety and upholding civil liberties and privacy rights. This balance requires a collaborative effort involving legal frameworks, community engagement, and continuous ethical assessment of AI applications in policing. The future of AI in law enforcement will thus be shaped not only by technological advancements but also by the societal, legal, and ethical contexts within which these tools are deployed. The goal remains to ensure public safety while respecting the rights and dignity of individuals and fostering a just and equitable society.