Cesar Fernandes – 2025 Annual Conference Student Scholar Reflection
Cesar Fernandes
Public Policy major, Southern University and A&M College
Artificial intelligence (AI) and data analytics are swiftly revolutionizing law enforcement in identifying trends, preventing incidents, and responding more effectively. Predictive policing is a key application, analyzing historical crime data alongside real-time information such as 911 calls, traffic data, and social media activity to anticipate potential hotspots. This approach allows law enforcement to strategically position their officers where they are needed most, improving efficiency and reducing response times.
In fact, from a public risk management perspective, this is a game changer. By allocating resources more strategically, we can reduce violence, minimize public disturbances, and prevent property damage, directly impacting the financial health of cities, counties, and insurers. AI can foster more objective decision-making and help law enforcement reduce human bias when used ethically and transparently.
Let us now talk about real-time surveillance and automated surveillance.
AI-powered surveillance systems represent another significant advancement. These systems include facial recognition, license plate readers, body cameras with automatic incident detection, and citywide camera networks. With these tools, police departments can:
- Identify suspects in real-time
- Locate stolen vehicles or persons of interest
- Monitor crowds at protests or public events
- Receive automatic alerts of suspicious activity
These technologies contribute to crime prevention and rapid emergency response, creating a digital record that increases transparency and accountability. This allows for better documentation of incidents and provides insurers with a better defense against exaggerated or false claims. Protecting privacy and community monitoring can strengthen trust in law enforcement.
Risk-Based Resource Management
AI is not just a tool for emergency response; it also improves future planning for police departments. With increasingly frequent budget cuts and staff shortages in many areas, AI helps police departments make smarter, risk-based decisions. By analyzing factors such as time of day, location, community characteristics, and seasons, police departments can better dispatch patrols, adjust work schedules, and improve community engagement. This data-driven approach improves public risk management and increases department efficiency while controlling costs. As a result, insurers can begin developing coverage options that reward innovative technologies, proactive risk management, and better documentation practices.
The Ethical and Legal Dilemma
However, the rise of AI is not without its challenges. It can exacerbate existing inequalities, undermine privacy, and even cause harm if misused. Predictive models that rely on biased historical data could reinforce systemic injustices. Surveillance tools could be misused without adequate oversight. Furthermore, identifying those responsible when automated systems make mistakes can be challenging. Public risk managers must work closely with law enforcement to ensure that:
- Transparency of AI algorithms.
- Community involvement in the adoption of new technologies.
- Strict rules regarding data use and storage.
- Clear legal frameworks for oversight and accountability.
The success of AI depends on the quality of the data it uses and the intentions behind its application. Risk professionals must anticipate how AI can solve problems and recognize how, if misused, it could create new ones.
A Call to the Public and Risk Community
So, what role should we all play in shaping this future?
As public risk, insurance, and governance professionals, we must consider whether we can manage the legal, reputational, and ethical risks associated with AI-driven policing. As community members, we should consider how to balance safety and privacy with innovation and accountability.
You are invited to take a moment to think about and discuss these important questions:
Should law enforcement rely on predictive analytics to prevent potential threats to the public?
What kind of community oversight is essential for ensuring that AI is used fairly and responsibly?
How can risk managers and insurers foster safe and transparent innovation in public safety?
Cesar Fernandes
Public Policy major, Southern University and A&M College
Cesar Fernandes is a dedicated public policy professional with expertise in peacebuilding, governance and international business affairs. Cesar is a doctoral candidate in public policy at Southern University. He holds an MBA in finance, human resources and supply chain management from Southern University and A&M College and a Bachelor of Science in marketing and international affairs from Cheikh Anta Diop University (UCAD)—École Supérieure Polytechnique (ESP). He also completed intensive training in peacebuilding program design, implementation, monitoring and evaluation training at the Kofi Annan International Peacekeeping Training Centre. With over a decade of experience, Cesar has held key positions, including national coordinator for the West Africa Network for Peacebuilding in Guinea-Bissau, where he managed multiple peacebuilding programs. Through his leadership, he continues to drive impactful projects that promote education, governance and economic development.
