How Artificial Intelligence Technology is Changing Law Enforcement

Artificial intelligence (AI) is at the forefront of technology, and law enforcement is no exception. AI integration with policing technology offers promising advancements in efficiency and effectiveness. However, it also presents significant challenges that must be carefully navigated. Challenges that include protecting the privacy of the public and officers, following all applicable laws, and remaining transparent with the community and within the agency.  

AI is becoming more prevalent in law enforcement products and solutions and encompasses a range of technologies that enable machines to perform tasks requiring human intelligence, such as learning, reasoning, and problem-solving. The Department of Justice released the final report on Artificial Intelligence and Criminal Justice in December 2024, identifying the four uses of AI in the criminal justice system. The four categories included in the report encompass the various AI technologies used by law enforcement. 

1. Identification and surveillance: The DOJ report states that using AI to recognize faces, fingerprints, and other biometric identification can be “significantly more accurate and efficient than human observations and comparisons, especially related to errors, bias, and privacy.” 
2. Forensic analysis: AI speeds up forensic processing and can also be used to make connections and compare analyses. Forensic results must meet high evidence standards, so testing for accuracy and maintaining transparency in analysis is critical.  
3. Predictive policing: AI can use historical data to forecast the likelihood of criminal activity, but there are inherent risks associated with what was known as predictive policing. The quality of data used, time frames tracked, validity of models used, privacy concerns, and other factors can deter this type of AI technology. As AI and machine learning improve, the predictive part of policing is often replaced by evidence-based methodology. 
4. Risk assessment: This AI application has been used in some aspects of prisoner incarceration and sentencing, yet these applications can be problematic and less than transparent. 

These applications can significantly enhance law enforcement capabilities but also raise important considerations. 

Data quality and bias mitigation 

AI systems rely heavily on data, and the quality, accuracy, and representativeness of this data are crucial. The National Conference of State Legislatures tracks AI legislation across the states and at the Federal level and emphasizes addressing bias in AI applications to prevent discriminatory outcomes. Biased or incomplete data can lead to skewed outcomes, potentially perpetuating or exacerbating existing disparities. If historical crime data reflects systemic biases, policing algorithms may disproportionately target specific communities. To mitigate this risk, agencies should: 

• Ensure data diversity by collecting data from many diversified sources to capture a comprehensive picture. 
• Regularly audit the technology by verifying the data and outcomes and working with technology vendors to conduct routine checks to identify and correct biases in AI systems. 
• Implement transparency measures by maintaining clear documentation of data sources and decision-making processes to facilitate accountability shared internally with agency staff and externally with the community and elected officials. 

Considerations when using AI 

Artificial intelligence in law enforcement technology continues to grow. As AI becomes more prevalent and known throughout communities, it is imperative that agencies hold themselves to high standards when implementing and using AI technologies.  

• Ethics and privacy: The deployment of AI in law enforcement must align with ethical standards and legal frameworks. How each agency handles privacy rights may not align, but balancing an individual’s right to privacy against the benefits of surveillance technologies needs to be determined. Agencies should also hold themselves accountable for using AI technology, exercise transparency within their community, and remain vigilant about the data so bias is not included.  
• Technical infrastructure and expertise: Implementing AI requires robust technical infrastructure and skilled personnel. Agencies should assess their current capabilities and identify gaps in hardware and software to ensure the AI technology can operate as intended. The agency should develop a data management system that can handle large amounts of data securely and efficiently. Equally important, agencies should invest in training staff to use and manage AI technology tools effectively. 
• Community engagement and transparency: Building public trust and maintaining transparency is essential for successfully adopting AI in law enforcement. Agencies should engage with communities to educate the public on how the AI tools are used along with the benefits they bring. Asking the community for their thoughts on the agency’s technology plan and strategy lets them share concerns that the agency can respond to while showing transparency and accountability. 
• Monitoring and evaluation: AI systems should be monitored and evaluated regularly to make sure they are working as intended. Establishing performance metrics and regular audits allows the agency to review AI technology’s performance and note any areas for improvement.  

AI technology is here to stay 

Integrating AI into law enforcement holds significant promise for enhancing public and officer safety through situational awareness and increasing operational efficiency. However, it requires careful consideration of data quality, ethical and legal standards, technical infrastructure, community relations, and ongoing oversight. By thoughtfully addressing these areas, law enforcement agencies and their communities can benefit from artificial intelligence and machine learning technologies.