Can Machine Learning Be Applied In Public Administration?
Machine Learning in Public administration: Those who still don’t know may be scared by the term; after all, machine learning in free translation means “the learning of machines. “
However, far from science fiction movies, the technology that allows machine learning to happen is essential for the advancement of automated solutions that aim to meet the high demand of the public and private sector with much more agility and effectiveness.
Gradually, digital transformation has been present in projects and public administrations to make processes more agile, transparent, and less bureaucratic. The Digital Government Law reflects how technology has contributed to this evolution, being highly positive both for management and the general population.
In this article, you will understand what machine learning is and how this technology can be used in the public sector in practice. Continue reading and clear all your doubts.
What Is Machine Learning?
By definition, machine learning is the term used to name the area of artificial intelligence responsible for developing algorithms capable of teaching a machine to perform tasks automatically.
With machine learning, it is possible to identify and reproduce patterns from a large volume of data to compose a learning model. What does this mean in practice? From technology and data observation, machines become capable of reproducing examples, direct experiences, or by instruction without the need for new programming.
The central idea of this technology is that, once the machine learns, it can delegate simple, complex, or dynamic tasks to it and gain more predictability about the expected results intelligently.
In practice, this means that, with the correct use of machine learning in public administration, it is expected that it will be possible to analyze a large volume of data with much more agility, even if the complexity is high.
The consequence is more accurate and practically real-time results, contributing to more precise decision-making, reducing human interventions and the risk of misinterpretation. In other words, machine learning offers much faster and more accurate data analysis, making it a highly qualified investment.
How To Apply Machine Learning In Public Administration?
When applied in the public sector, machine learning technology can be an excellent ally for managers who seek to reduce bureaucracy in service, optimize processes and provide more transparency in their government.
For example, Education line combines machine learning with other technologies, such as the internet of things, facial and voice recognition, and artificial intelligence. In this way, it allows the administrative and pedagogical management of schools, as well as the performance of students, to have more flexibility and agility in the processes, monitoring from the students’ food to the relationship between parents and teachers.
In practice, in a scenario like this, Machine Learning technology facilitates decision-making in a didactic and agile way, since with it, it is possible to analyze the risk of evasion and failure, in addition to issuing reports and graphs that facilitate decision-making. Important decisions related to public education.
Another practical application that machine learning can contribute to public management within the school sector is the ease of centralizing information for access by the secretary of education, principals, and teachers. With a system like this, municipal education information can track each student’s data, all in one place.
According to product specialist Daniel, it was necessary to register at least 1,000 enrollments from previous academic years with at least 2% dropout or failure. “From the moment the feature is activated, new analyzes are carried out fortnightly. In this way, it is possible to follow the evolution of the data, delivering increasingly accurate numbers to teachers and managers“.
This behavior was necessary so that the algorithm could study the data of students enrolled in previous years, learn about them and detect threats.
With the tests completed, the final result showed an accuracy of 92% in cases of evasion and 85% in cases of failure at the end of the first evaluation period.
It is essential to highlight that because data power robots, they must be updated with factual information; this will make the results more accurate, allowing new actions to reverse the situation to be taken in advance.
Also Read: Here’s How To Use Machine Learning In Sales
