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By: Yusuf Yau
21/01/2024
Opportunities to creatively address persistent societal concerns are emerging as a result of the exponential growth of technology. The field of community security, which aims to improve the health, safety, and tranquility of urban residents and workers, is one that is ripe for change. Local human efforts can be greatly enhanced by artificial intelligence, which has the ability to decrease property and violent crime, streamline emergency response, and promote stronger social bonds.
Limited personnel budgets have always made it difficult for law enforcement agencies to deploy patrol officers effectively. When police have a large area to patrol, it can be difficult for them to be present at every single location at once. This means that criminal hotspots may remain unchecked while offenders take advantage of what they see as coverage gaps. In response to this issue, several innovative police agencies have used predictive policing systems driven by artificial intelligence to direct data-driven, targeted deployments.
These systems work by evaluating previous crime data combined with additional contextual factors, including locations, times of day, weather patterns, adjacent amenities, and more. Then, using historical patterns, sophisticated machine learning algorithms may determine statistical correlations between these factors and pinpoint hotspots for criminal activity. Using these probabilistic models, some have successfully predicted specific types of property and violent crimes up to seven weeks in advance.
With hotspot maps in hand, police chiefs may strategically focus vehicle patrols, foot patrols, and surveillance cameras on the highest danger zones to maximize visible deterrence. Studies have shown these predictive analytics techniques can concentrate limited law enforcement resources for up to 30% higher disruption and avoidance of projected occurrences. Of course, biases in training data must be properly examined and corrected, but when implemented sensibly, predictive policing offers a significant force multiplier impact.
Traffic safety is another key societal concern that AI is making meaningful gains in solving. According to the World Health Organization, about 1.3 million people die in road accidents globally each year, with tens of millions more injured or disabled. The main driver is improper traffic light scheduling, failing to optimize vehicle movements. However, computer vision and sensor technologies are enabling self-learning intelligent transportation systems to revolutionize signal coordination.
At the intersection level, AI-powered adaptive traffic control continuously evaluates current vehicle and pedestrian volumes using an integrated roadside sensor. Sophisticated algorithms then dynamically re-time light cycles and phase sequences hundreds of times daily to smooth traffic flows and alleviate congestion. Some cities deploying these systems have seen accident rates reduce by 30–40% as AI eliminates inefficient static timetables that back traffic up. Coordinating signals across wider networks significantly decreases drive times and carbon emissions.
Meanwhile, AI-assisted autonomous vehicles promise to eventually eliminate human error—responsible for over 90% of crashes—from the driving task. With 360-degree perception, computer reactions measured in milliseconds, and rigorous adherence to traffic restrictions, self-driving cars produced through machine learning stand to make roadways substantially safer. Though legislative and technological challenges remain before broad adoption, even advanced driver aid systems integrating AI are already avoiding accidents, according to some automakers.
Public health emergencies like disease epidemics also provoke feelings of uneasiness within communities when risks spread anonymously. Yet AI demonstrates the ability to boost healthcare staff's response capability. For instance, computer vision mixed with infrared cameras provides contactless screening of crowds at areas like airports, train stations, hospitals, and stadiums. By sensing increased body temperatures—an early indication of infections—these systems may help identify infected persons needing testing or treatment sooner.
AI has also benefited epidemiologists in tracing illness transmission patterns. By processing enormous troves of anonymized individual movement data from smartphones and credit cards, algorithms learned to estimate localized COVID-19 infection increases up to a week in advance with over 80% accuracy, according to one study. Armed with these projections, health departments might pre-position staff and supplies, provide early warnings to at-risk groups, or advise on containment measures. AI thereby supplements human resources for swifter, more data-driven public health solutions.
When situations do occur, every second matters in preserving lives. Yet coordinating first responders across numerous agencies and jurisdictions remains a coordination difficulty, even for emergency call centers. Here, AI is expediting the process using technologies like computer-aided dispatch. These systems automatically route the closest and best-equipped ambulance, fire truck, or police unit to 911 calls based on real-time tracking of all response vehicles and dynamic traffic circumstances.
Studies demonstrate AI-powered dispatch can shave vital minutes off emergency response's times compared to manual approaches. Additional AI capabilities are helping call takers to more swiftly collect key injury or threat facts, then lead callers in life-saving actions like CPR instructions until help arrives. With AI quickening coordination, communities receive the benefits of speedier reaction, more effective triage of large occurrences, and improved results. Over the long run, this can translate to major cost reductions for public safety budgets too.
On the ground, AI is likewise proving a force multiplier for overextended security forces charged with monitoring public places. Body-worn cameras linked with facial recognition help identify persons of interest, while object detection highlights suspicious abandoned parcels or weapons for additional scrutiny. Live public surveillance camera feeds that would overwhelm human monitoring are no match for machine vision either. AI systems can track dozens of simultaneous views and inform guards quickly upon detection of altercations, medical issues, or other alarming situations.
Freed from constant monitoring, security staff can focus on de-escalation, outreach, and community policing responsibilities requiring human judgment. Meanwhile, AI functions as an extra set of automatic eyes, boosting scenario awareness. Some establishments have even experimented with robot security guards able to monitor wider areas and potentially risky spots like construction sites. When backed by human oversight, these apps harness technology to multiply on-the-ground security capabilities and coverage.
In fact, true community security also depends on strong social links that prohibit illicit behavior and create collective guardianship. Here, AI is displaying capabilities to bring neighborhoods closer together digitally and inspire grassroots participation. Hyperlocal social networks and next-door apps promote information sharing about local issues, preferred service providers, and lost pets. Neighborhood watch organizations are exploiting these platforms to arrange volunteer patrol coordination and encourage anonymous crime reporting.
AI is also enabling new community information services. Automated writing bots write daily newsletters, gathering notifications on construction, traffic, emergencies, or events from municipal data streams. This keeps citizens updated on issues directly touching their blocks and neighborhoods while building engagement. Some firms are even utilizing AI to automatically transcribe and broadcast video from police community meetings online for individuals unable to attend. As technology threads the social fabric more closely, it develops a common responsibility to watch out for one another.
In conclusion, while AI can never replace human interactions and alertness, these applications highlight its immense potential to decrease insecurity through the augmentation of community defense efforts. By boosting capabilities across law enforcement, transportation, public health, emergency response, and grassroots security and engagement, AI helps maximize the protection of lives, property, and well-being. It permits protection to expand beyond the limitations of human and financial resources alone. Of course, appropriate development and oversight ensuring benefit rather than harm are vital. But the merging of artificial and human intelligence promises major breakthroughs for establishing security worldwide when employed prudently and for the public good.
Yusuf Yau
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