The use of artificial intelligence can be carried out in all environments. The problem that is solved by PredPol can be viewed as a resource allocation problem. These kinds of problems occur in other sectors too. For example, in some environments where the speed and reliability of data are very important, the use of AI may be the optimal solution. The accuracy of the data requires a multitude of information and data backups. In the public sector, its data are very often stored and make it a usable source of information. In areas where emergencies are critical, resource allocation is very important. Resources must be placed in the right place to avoid unnecessary movement and non-use of resources. Let us take the example of the fire brigade. When we talk about new technology in this kind of situation we often think of the robot to perform dangerous actions. There are many possibilities to implement new technologies in a fire station. The most important thing in an urgent situation is to allocate the right number of resources but also the right type of resource. For example, for a small garbage fire, there is no need to send 3 trucks and 15 firefighters, this would put the area at risk because these firefighters could not be assigned to other emergencies. In the opposite way, if a fire projects onto an entire building, it will not be enough to send 1 truck with 5 firemen. To do this, it is necessary to have a certain mastery of these resources and a certain knowledge of the type of intervention. By com-bining data and analysing it, we can compare them with PredPol's technology. A project set up in partnership with the French fire brigade, in order to be able to anticipate their interventions, and thus adjust the number of personnel in time and space in order to avoid capacity saturation or excessive mobilization [Fortin 2018].
The use of the AI makes it possible to better anticipate the risk of unscheduled hospitali-zations of a given patient based on his or her characteristics. The team at the George Institute of Global Health at Oxford University has developed a tool to be more proactive
in hospitals. The objective of this study is to provide a tool that allows health professionals to accurately manage the risk to their patients and, as a result, make better screening de-cisions and be proactive in providing care that could reduce the workload of emergency room admissions [Wunderlich et al. 1996].
The use of AI is now increasingly used in the private sector. Capgemini, a technology consulting company, has developed a system for the recruitment and allocation of human resources according to specific missions. The computer examines, on the one hand, the work to be done and, on the other hand, the available staff or potential candidates and tries to match them as closely as possible. As a result, staff with expertise in certain areas are considered experts and are used for the best they can do and not for their versatility.
At this system, we can discover that there are some gaps. Indeed, having a work team that is very competent in one field but has gaps in some other fields can be very dangerous.
Let's take as an example a team composed of programming experts but having no project management or management skills. This team will not be able to complete the task re-quested due to its lack of competence. In this situation, it is, of course, necessary to be careful to integrate a multitude of knowledge into a team and thus can develop experts in an increasing number of fields.
There is, therefore, a multitude of areas in which resources can be allocated through Ar-tificial intelligence. Some of these fields may have similar problems or completely dif-ferent problems for example personal rights violation. It has to be checked whether each of the respective cases is suitable and which variables can be used to solve the individual resource allocation problem.
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