AI and AI-Related Use Case Library for Community-Oriented Policing Services
Unlock value-driven AI use cases to transform your organization.
- AI integration in policing faces multifaceted challenges impacting its effectiveness and ethical implementation.
- Ensuring AI systems avoid discriminatory outcomes and address inherent biases is a pressing challenge.
- Balancing the needs for effective law enforcement with individuals' right to privacy remains a complex issue.
- Determining responsibility and accountability in cases of AI-related errors or misuse poses a significant challenge.
Our Advice
Critical Insight
- Limited access to diverse and unbiased data sets hampers the development of fair AI models.
- Gaining public confidence in AI-assisted policing is hindered by concerns about surveillance and misuse of personal data.
- Limited resources hinder the deployment of advanced AI systems, affecting both training and implementation.
- By ensuring the responsible and ethical use of AI in policing, and getting the public involved in AI in policing development, law enforcement agencies can harness its potential, while minimizing its pitfalls, and ultimately, enhance the effectiveness, efficiency, and accountability of law enforcement agencies, and the safety, security, and well-being of the society.
Impact and Result
- Info-Tech’s guidance provides for meticulous data curation, transparency, and ongoing bias mitigation efforts in AI model development.
- Within the context of the COPS Business Reference Architecture portfolio, this AI use case library:
- Identifies potential sources of value to strategically operationalize use case capabilities
- Jumpstarts the idea generation process during the capability development phase
- Provides next steps toward Ai-driven use case integration and implementation, and
- Builds-in safeguards to foster public trust and community engagement.
AI and AI-Related Use Case Library for Community-Oriented Policing Services
Unlock value-driven AI use cases to transform your organization.
Technology impacts every aspect of the law enforcement profession and is constantly evolving.
Analyst Perspective
AI has many applications for criminal justice and public safety, such as crime prevention and reduction, facial recognition and biometrics, drones for surveillance, and evidence processing.
AI in policing is a promising and powerful technology that can bring many benefits and opportunities to the field of criminal justice and public safety, but also some challenges and risks that need to be carefully considered and addressed. By ensuring the responsible and ethical use of AI in policing, law enforcement agencies can harness its potential, while minimizing its pitfalls, and ultimately enhance the effectiveness, efficiency, and accountability of law enforcement agencies and the safety, security, and wellbeing of the communities they serve.
This research presents some of the benefits and challenges of AI in policing and suggests ways to ensure its responsible and ethical use.
Neal Rosenblatt
Principal Research Director
Public Health Industry
Info-Tech Research Group
Executive Summary
| Your Challenge | Common Obstacles | Info-Tech's Approach |
| AI integration in policing faces multifaceted challenges impacting its effectiveness and ethical implementation.
Ensuring AI systems avoid discriminatory outcomes and address inherent biases is a pressing challenge. Balancing the needs for effective law enforcement with individuals' right to privacy remains a complex issue. Determining responsibility and accountability in cases of AI-related errors or misuse poses a significant challenge. |
Limited access to diverse and unbiased data sets hampers the development of fair AI models.
Gaining public confidence in AI-assisted policing is hindered by concerns about surveillance and misuse of personal data. Limited resources hinder the deployment of advanced AI systems, affecting both training and implementation. |
Info-Tech's guidance provides for meticulous data curation, transparency, and ongoing bias mitigation efforts in AI model development.
Within the context of the COPS Business Reference Architecture Portfolio, this AI use case library:
|
Info-Tech Insight
By ensuring the responsible and ethical use of AI in policing, and getting the public involved in AI in policing development, law enforcement agencies can harness its potential, while minimizing its pitfalls, and ultimately enhance the effectiveness, efficiency, and accountability of law enforcement agencies and the safety, security, and wellbeing of the society.
Section 1
Overview: AI in Policing
Overview
AI in Policing – Benefits and Challenges
Public safety and criminal justice are benefiting from AI, as it offers new opportunities and solutions to address some of the most pressing challenges and needs in the field. However, AI also raises some concerns and questions, as it may have unintended or undesirable consequences and impacts on human rights, civil liberties, and social justice.
Benefits > Challenges > Responsible Use
There is a lot of mistrust between communities and the police, and what we have seen again and again is that traditionally marginalized low-income communities are less likely to call for help. Introducing technology like gunshot detection empowers your police officers and law enforcement agencies to respond and help the community.
‒ Jeff Merritt, Head of IoT and Urban Transformation at The World Economic Forum
Source: World Economic Forum, 2024.
Prevent and reduce crime rates
One of the main benefits of AI in policing is that it can help prevent and reduce crime rates by analyzing large amounts of data and identifying patterns, trends, and anomalies.
Forecasting
AI can be used to forecast where and when crimes are likely to occur, based on historical and real-time data, and to allocate policing resources accordingly.
Tailored interventions
AI can also help identify the potential for an individual under criminal justice supervision to reoffend, and to provide tailored interventions and support.
Evidence processing and analysis
AI can assist in solving crimes and bringing offenders to justice, by enhancing the capabilities and efficiency of evidence processing and analysis (e.g. AI can be used to identify individuals and their actions in videos relating to criminal activity or public safety, to analyze DNA samples and match them to suspects or victims, and to detect gunshots and locate their sources).
Using AI in Facial Recognition Analytics
Improve performance and accountability
Another benefit of AI in policing is that it can improve the performance and accountability of law enforcement agencies by providing them with more accurate and reliable information, tools, and feedback.
Monitoring and evaluation
AI can be used to monitor and evaluate the behavior and performance of police officers and to provide them with training and guidance.
Reduce human error and biases
AI can also help reduce human errors and biases, by providing objective and consistent assessments and decisions.
Transparency and legitimacy
AI can also enhance the transparency and legitimacy of policing, by enabling more effective communication and collaboration with the public and other stakeholders (e.g. AI can be used to provide citizens with access to relevant and timely information, to solicit their feedback and input, and to address their complaints and concerns).
Using Drones in Targeted Surveillance Analysis
Design AI into policing for value-driven outcomes
AI can act as a force multiplier, augmenting police staff and improving accessibility, while collaboration is key for leveraging shared AI infrastructures.
AI can revolutionize police operations, enhancing police services, internal efficiency, data analysis, and creativity.
Enhanced community trust with the implementation of responsible AI strategies that consider fairness, reliability, accountability, privacy, inclusiveness, and transparency.
Responsible approaches to AI are crucial, requiring the adoption of strategies, policies, training, and accountability in public sector organizations.
Community engagement can play a pivotal role in shaping the use of AI in police departments.
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Unlock Sample ResearchContributors
- Blayne Eliuk, Alberta Law Enforcement Response Teams (ALERT), Director of Technology & Investigative Support
- Scott Gagnon, Alberta Law Enforcement Response Teams (ALERT), Manager of Application Development & Support
- Brent Dyer, Calgary Police Service, Executive Director, IT & Infrastructure Division
- Sam Fessehatsion, Calgary Police Service, Architect Analyst
- Joyce Dufresne, Edmonton Police Service, Administrative Manager
- Paul Fahey, Edmonton Police Service, Senior Architect
- Erran Milligan, Edmonton Police Service, Team Lead, Business Technology Transformation Unit
- Norman Mendoza, Edmonton Police Service, Director, Architecture & Solutions Branch
- Jonathan Green, Guelph Police Service, Manager of Information Systems Services
- Akram Askoul, Niagara Regional Police, Director of Technology Services
- Joe Couto, Ontario Assoc of Chiefs of Police (OACP), Director of Government Relations and Communications
- Anna Beatty, Ottawa Police Service, Chief Information Officer
- Elizabeth Izaguirre, Ottawa Police Service, Manager, Business Intelligence
- Cameron Hopgood, Ottawa Police Service, Director of Strategy
- Tony Ventura, Peel Regional Police (PRP), Director of Information Technology Services
- Alpha Chan, Toronto Police Service (TPS), Chief Information Security Officer
- Billy Zhou, Toronto Police Service (TPS), Acting Manager, Enterprise Architecture, Quality Assurance, and IT Risk Management
- Raymond Lai, Vancouver Police Department, Director, Information & Communications Technology
- Micheline Manseau, York Regional Police (YRP), Director of Information Technology
- Benny Zeng, York Regional Police (YRP), Acting Director of Information Technology