After having defined the concepts inherent to Predictive Policing in the first part of the topic,, In this second part of the topic we will explain how this Policing model can be implemented in the Security Forces (FS), as well as its application in the prevention of accidents.
Rogério COPETO
Colonel of the GNR
Master in Law and Security and Homeland Security Auditor
Beyond the obstacles, already identified, the implementation of Predictive Policing by the FS, its non-implementation may also be justified by the limitation of available resources, taking into account that they have limited budgets and resources to invest in advanced technologies, training and infrastructure necessary to implement this policing model.
Resistance to change can also be an obstacle, because adopting new technologies and strategies is not always easy, especially in more traditional and conservative organizations such as FS, where institutional culture may be resistant to significant change.
Ethical and privacy issues, as already mentioned, Taking into account the use of personal data to predict crimes raises ethical and privacy concerns, which can result in resistance on the part of the population, which could lead to a lack of public trust necessary for Predictive Policing to function effectively, the population's trust being fundamental, because if the public does not trust technology or decisions based on it, your acceptance will be limited.
On the other hand, Predictive Policing algorithms are not always 100% accurate, which can result in false positives and false negatives, raising doubts about its effectiveness, being necessary for successful implementation of these policing models, specialized training for all FS elements, which can be time-consuming and expensive.
The lack of clear regulations and specific legislation for the use of Predictive Policing technologies makes their implementation challenging, although this policing model has the potential to improve the efficiency and effectiveness of the FS, where overcoming these obstacles and challenges is essential for broader and more successful adoption, It is essential to balance benefits with legal concerns, ethics and privacy, ensuring technology is used responsibly and fairly.
As already mentioned, Predictive Policing uses algorithms and data analysis to identify the areas and times where crimes are most likely to occur., resulting in the creation of profiles, based on historical information, such as types of crimes, locations and date/time, It is important to note that these profiles are not about individuals, but rather about crime patterns in a given area, whose main idea is to allocate police resources more effectively, increasing presence where crimes are most likely to occur, and not the creation of suspect profiles.
Yet, It is important to respect privacy and avoid discriminatory or unfair use of data in Predictive Policing, the focus being crime prevention in certain areas.
However, Predictive Policing can also be used to prevent road accidents through the analysis of historical data., with the aim of identifying patterns and areas of greater risk of accidents, This makes it possible to predict the places and times most prone to accidents., allowing the FS to intensify presence and supervision in these most critical locations, contributing to the deterrence of risky behavior and increasing police visibility where it is most needed, helping to reduce serious road accidents.
So, the FS as supervisory entities with responsibilities in road prevention, allows them to obtain historical data on infractions and accidents, that associated with weather conditions, roads and vehicles, and information collected by traffic control camera systems can also be integrated, that monitor traffic flows, particularly in large cities, have the necessary information to feed Predictive Policing algorithms, in order to identify patterns that may indicate areas of high risk of accidents.
Predictive models will then be able to predict the places and times with the highest probability of accidents occurring, and they must be updated through the constant introduction of new data, whose results can be integrated into the information systems already in use by the FS.
To achieve this, it is necessary to provide training to FS elements, in order to understand and efficiently use the information provided by predictive models, procedures must be established for preventive actions to be carried out, based on predictions, such as increased police presence and intensified inspection in areas of greatest risk.
To enhance results, the population must be informed about the advantages of Predictive Policing, whose efforts are to increase road safety, as well as drivers must be informed about the areas of greatest risk, to promote safe behavior and defensive driving, collaboration with technology companies may be necessary, in developing innovative solutions, namely applications that alert drivers about the roads and times of greatest risk in real time.
As with other policing models, This type of policing must also be evaluated, For this reason, performance metrics must be implemented to assess their effectiveness in preventing crime and road accidents, as well as carrying out regular assessments, with the aim of adjusting strategies, whenever necessary to improve the results obtained.
In summary and to implement Predictive Policing effectively, to predict the occurrence of crimes and road accidents, It is necessary to consider several needs, namely high quality data, essential to power predictive algorithms, transparency about how algorithms are used and how data is treated, training FS elements to understand Predictive Policing tools, continuous assessment to mitigate bias and ensure results are fair and effective, not harming vulnerable groups, concluding that Predictive Policing is an approach with the potential to improve the efficiency and effectiveness of FS, with real consequences for people’s safety.
