Ogirko, Ihor; Shapenko, Liudmyla; Zolotar, Olha (2025). AI AND DATA ANALYTICS IN PUBLIC ADMINISTRATION: TOWARD RESPONSIBLE AND SECURE IMPLEMENTATION. Social and Human Sciences. Polish-Ukrainian scientific journal (https://issn2391-4165.webnode.com.ua/), 01 (45).
AI AND DATA ANALYTICS IN PUBLIC ADMINISTRATION: TOWARD RESPONSIBLE AND SECURE IMPLEMENTATION
Ogirko, Ihor, Professor, Doctor of Physical and Mathematical Sciences, Institute of Printing and Media Technologies, Lviv Polytechnic National University (Ukraine, Lviv), ORCID: https://orcid.org/0000-0003-1651-3612 ;
Shapenko,
Liudmyla, Ph.D (Law), Associate Professor, Kyiv
National Aviation Institute (Ukraine, Kyiv), ORCID: https://orcid.org/0000-0001-7351-641X ;
Zolotar, Olha, Doctor of
Law Science, head of the scientific
laboratory, Scientific Research Institute of Informatics and Law at the
National Academy of Legal Sciences of Ukraine (Ukraine, Kyiv), ORCID: https://orcid.org/0000-0002-2461-6745
The article examines the peculiarities of artificial intelligence (AI) implementation in the conditions of digital transformation of society and the development of new technologies as one of the of the key tools for improving the efficiency of management, decision-making and decision-making and process automation. The author emphasizes the importance of AI integration integration to optimize the quality and efficiency of management decisions. The study identifies identifies the main areas of AI application, in particular: big data analysis for trend forecasting, process automation, implementation of intelligent decision support systems, and the use of chatbots. The research also includes analysis of the specifics of AI implementation, taking into account the state of digital transformation in the country and the current legislative framework. It is noted that an important role is played by is the development of a legal framework for regulating the use of AI in interaction with the latest technologies. At the same time, the article analyzes the implementation of AI and and the problems of artificial intelligence development are investigated. It is emphasized that effective implementation of AI in Ukraine is possible only if it is in line with international standards.
The practical possibilities of artificial intelligence that can be
effectively used in modern society are investigated. that can be effectively
used in modern society. The main areas in which artificial intelligence is used
are analyzed in which artificial intelligence is used are analyzed. It is
emphasized that the issue of using of artificial intelligence technologies in
organizational development should be considered in two ways: on the one hand,
the organization should be prepared for the introduction of technological
innovations, and on the other hand, AI-related solutions must meet the
requirements of organizational development of organizational development and
meet its needs.
The article describes the concept of using AI to solve problems and
improve organizational development processes, the main purpose of which is to
propose the use of certain technologies and methods. Among the obstacles to
accelerating the formation of Society 5.0 in Ukraine are Russia's armed
aggression against Ukraine, which is qualified as a war of aggression and has
large-scale global consequences, imperfect government institutions,
insufficient efficiency of lawmaking, technological lag, low qualification of
human resources, and weak support for innovative changes in society.
For society to accept innovative changes, it is necessary to pay
attention to the development of education and science, and to support the
formation of digital entrepreneurship. Today, the organization of governance is
largely dependent on the information economy, which is developing thanks to
digital technologies. The characteristics of the new information environment
include the increasing role of information and knowledge in society, which are
becoming key factors in economic development. Thus, with the advent of
technologies such as the telephone, computer, Internet, and artificial
intelligence, marketing has changed significantly.
The study analyzes the legal issues of digitalization in the
environmental sphere in relation to the relevant processes in the economic
sphere. The study finds that the digitalization of environmental protection
requires solving institutional, infrastructural, ecosystem and automation
problems, with the legal factor playing a crucial role. The factors of digitalization
should be taken into account not only in strategic, forecasting and program
documents, but also in legal documents, in particular in regulations.
At the same time, while emphasizing the benefits of digitalization, we
should not neglect its possible negative consequences, especially in the field
of ecology.
Keywords: artificial intelligence, AI, administrative processes,
development problems, technologies, resources, management, development, data
mining, automation.
Problem statement. In the modern
context of technological transformation, artificial intelligence acts as a tool
that can significantly increase the efficiency of management processes. The
relevance of this study lies in the need to determine the essence and specifics
of the integration of artificial intelligence into the public administration
system, as well as to analyze its potential to ensure the effectiveness of
management decisions.
Analysis of recent research and publications.
Artificial intelligence issues are considered in scientific research by
specialists in various fields, including management, law, economics, and other
fields. The essence of artificial intelligence, its mechanisms, characteristic
features and classification are studied by the following Ukrainian scientists [1;
2; 3; 4; 5; 6; 7; 8; 9; 10; 11], how A. Kolesnikova, O. Karapetian, A.
Pohorelenko, T. Yarovoho, L. Zhyvtsovoiu, O. Skitsko, P. Skladannyi, R.
Shyrshov, M. Humeniuk, M. Vorokhob, R. Kvasnytska, N. Skopenko, S. Tsyhanov, O.
Petriv, Yu. Karpenko, V. Vitlinskyi, D. Pchelianskyi, S. Voinova, N. Reznikova.
Among foreign scientists who pay considerable attention to the study of AI
problems, it should be noted Work А. Vitforda, L. Anastasopulosa, S. Asha, T.
Shanafelta, B. Virtsa, Kh. Esha, Kh. Mekhra, S. Lina, and others. Despite the
significant developments of scientists in the context of this problem, the key
topics of serious scientific discussions remain the study of a number of
challenges related to the implementation of AI in the field of management.
The purpose of the article.
The main idea of the article is to analyze the problems and challenges
associated with the development and application of artificial intelligence
technologies in managerial activities.
Presentation of the main material.
Artificial intelligence is a branch of computer science that deals with the formalization of problems and tasks that mimic actions that are inherent in humans. Nowadays, AI is an integral part of human life: it helps to study, work, and solve extremely complex problems in various fields. Modern machines are able to recognize speech, have technical vision, which allows you to accurately determine a person's age, gender, emotions, identify objects, and more. AI is widely used in various sectors of modern society, including finance, healthcare, education, transport, weapons and military equipment. It is used for medical diagnostics, e-commerce, remote control, and Earth sensing.
Today, AI is able to influence the information space in the largest forms.
Therefore, its implementation and use must be socially oriented and meet the
interests of human security, as well as the protection of his personal space.
In today's society, one of the biggest threats to AI is its impact on
security. This prompted the international organization UNESCO to develop a
standard of ethics for artificial intelligence, which was adopted by 193 member
states of this organization. This approach is aimed at preventing the
potentially negative impact of AI on the processes taking place in the world
community. The UNESCO Ethics Standard defines the basic principles, including:
protection of human rights, freedoms and dignity; promoting peace, justice and
inclusion; conservation of diversity and protection of ecosystems.
AI technologies are developing and implementing
extremely quickly. There are more than 2000 IT companies in the country engaged
in software development and specializing in the development of AI. At the same
time, Ukraine has made significant progress in implementing an open data access
policy.
As of 2023, according to the Open Data Index, Ukraine
ranks 31st in the world [1; 2; 3; 4]. Despite the fact that Ukraine has not yet
achieved a leading position in the field of artificial intelligence, it has
every chance to do so. Systemic support for the development of artificial
intelligence, which provides for an increase in public and private funding for
research, the creation of a favorable investment climate, and support for
innovative startups, can contribute to a significant strengthening of Ukraine's
position in the global AI market and positive changes in this area [2]. The
National Academy of Sciences of Ukraine, the Ministry of Digital Transformation
of Ukraine, the Ministry of Education and Science of Ukraine, the Ministry of
Strategic Industries of Ukraine and other government organizations of Ukraine
are involved in the process of creating the National Strategy for the
Development of Artificial Intelligence in Ukraine [3; 4; 5; 6]. In Ukraine,
practical experience in the use and implementation of artificial intelligence
is intensively accumulated in order to improve the quality of services and
solve current problems [1; 2; 3; 4]. Thus, together with citizens and the Kyiv
City State Administration, artificial intelligence developers are working on
the creation of urban digital services based on artificial intelligence
technologies in order to develop smart infrastructure in the capital. The Kyiv
Artificial Intelligence project team conducts research on ethics and
discrimination in the development of AI-based services in various fields, as
well as initiates discussions on the protection of digital rights of
Ukrainians. The Ministry of Digital Transformation of Ukraine is developing an
educational series on artificial intelligence, which explains the key
characteristics and problems associated with the creation and application of
automated systems. Some of the lectures are aimed at a younger audience, while
online courses are addressed to industry specialists. In particular, the
programs pay attention to the issues of observance of human rights in the
process of development of modern technologies. Within the framework of the
educational program, it is also worth noting the announcement of further
cooperation between the Ministry of Digital Transformation of Ukraine and
Google Ukraine. The companies agreed to create courses on the use of AI for
business development, public service and personal branding [2; 3; 4; 5; 6; 7].
However, there is a lack of appropriate legal standards governing the development
and implementation of such technologies in both the public and private sectors.
Artificial intelligence is a branch
of computer science that is concerned with creating machines capable of
performing tasks that would normally require the use of human intelligence.
This includes tasks such as language comprehension, pattern recognition,
learning, and planning. Of particular interest is the section on learning
models, where the term "machine learning" (ML) is the
main concept. Machine learning is a subfield of artificial intelligence,
which focuses on developing algorithms capable of learning from data without
the need for additional programming. Machine learning systems analyze large
amounts of structured and unstructured data to identify patterns and improve
their performance. In machine learning, models use statistical methods to make predictions or
make decisions based on the analyzed data. The areas of application of machine
learning cover a wide range of fields: from automatic translation of languages
and recommendation systems to pattern recognition and autonomous driving. Thus,
machine learning is one of the subfields of artificial intelligence. Trained
models are capable of making predictions. One of the areas of machine learning
is deep
learning. These concepts are similar and are often used
interchangeably, although there are some conceptual differences between them.
Deep learning is a subset of machine learning
that is based on the use of complex models called neural networks. These
models are able to learn and improve independently by analyzing
large amounts
of data
(Big Data).
The concept of neural networks or neural networks is key to
further research. Neural networks are a class of machine learning algorithms
that mimic the structure of the brain using layers of interconnected nodes, or
"neurons". They are the basis for deep learning, which allows systems
to learn and analyze on very complex data.
The Data Science & Intelligent Systems program
includes the following areas:
1.
Machine
learning and artificial intelligence.
2.
Data
analytics.
3.
Internet
of Things.
4.
Smart
solutions and systems.
To obtain a qualitative
forecast, you need a large amount of data and a data markup mechanism that provides structuring, categorization,
and annotation of data. First, the amount of data matters. Big Data is a term describing vast,
diverse, and fast-changing amounts of data collected from a variety of sources,
including social media, sensors, mobile devices, or e-commerce, and can be used
to analyze and solve complex problems in areas such as business, medicine,
science, government, and technology. Big Data is understood as data that has a
large number of records (millions, billions, or more). With the help of artificial
intelligence algorithms, the system analyzes data on aid recipients and
identifies potential violations and misuse of resources.
However, the use
of such systems carries significant risks. In particular, the creation of
profiles of vulnerable populations can lead to discrimination and human rights
violations. A change in the political situation or aggravation of social
problems can lead to the fact that individuals will be on the lists of persons
subject to prosecution due to unlawful data processing. In addition, there is a
risk of violating European data protection standards and non-discrimination.
Similar problems arise in other areas, in particular in the field of
healthcare, lending, employment, where automated decision-making systems can
create additional threats. For example, the Ministry of Justice of Ukraine uses
artificial intelligence to assess the risk of recurrence of criminals, and the
National Agency for the Prevention of Corruption (NACP) uses automated
verification of declarations. Although these systems are designed to increase
the efficiency of state bodies, they can also lead to human rights violations
and increased social inequality [7; 8; 9; 10].
Thus, banks can
use the personal profile of customers solely for the purpose of concluding
agreements on opening accounts, granting loans or purchasing securities, that
is, the list of potential risks is exhaustive and understandable. At the same
time, providing the state with profiles of vulnerable and marginalized groups
of the population is much more dangerous, since it is these groups that most
often receive social assistance. In the event of a change in political power or
aggravation of social problems, it is likely that individuals may be included
in the lists of persons who are threatened with persecution or repression. Therefore,
the majority of human rights defenders actively oppose not only state projects
in this direction, but also against the introduction of such systems in the
private sector. In addition, sooner or later the issue of compliance with
European standards will arise, especially in the field of personal data
protection and prevention of discrimination based on social status. These
challenges require special attention to prevent the negative consequences of
automation of vulnerable areas.
The new case management
tool allows you to create an electronic space for interaction between
recipients of social services, their providers, and case managers. Thanks to
this, the person who seeks help receives an individual approach and timely
support. Artificial intelligence significantly speeds up the processing of
requests, which allows you to quickly respond to people's needs.
Currently, the
Ministry of Justice of Ukraine uses the Cassandra software developed on the
basis of AI algorithms in test mode. This system determines a person's tendency
to repeat crimes (recidivism) using a questionnaire containing 97 questions.
The results of the analysis are integrated into the pre-trial report, improving
the validity of court decisions. Other machine learning algorithms of the
system are used to analyze large amounts of data, compare them with information
from different sources and identify signs of corruption offenses.
AI is also
actively used to monitor political advertising on the Internet, which allows
you to detect manipulation and disinformation. In many countries, police use AI
to track down criminals and identify suspects. In addition, AI is effectively
used to search for missing people [8]. For example, facial recognition
technologies have successfully helped to find dozens of people who disappeared
during military conflicts and natural disasters.
Despite the
active use of AI in management and the attempts of professional circles to
create legislative regulation of its application, many problems remain
unresolved. Given that artificial intelligence is an integral part of
information technology, two main groups of risks can be distinguished:
socio-economic and technological. Technological risks are associated with the
peculiarities of the use of information and communication technologies (ICT)
and software. Artificial intelligence and machine learning are interrelated,
but not identical, areas of information technology (IT) development: machine
learning is one of the key sub-fields of AI. Scientists use AI to analyze complex
scientific data, conduct simulations, and solve complex problems in various
fields, including physics, biology, and economics. With the help of AI, SMM
specialists (Social Media Marketing) can analyze large amounts of data from
social networks, which allows them to identify current trends, predict audience
behavior, and develop effective strategies for marketing campaigns.
In the field of programming, AI is used to automate
routine tasks such as writing code, testing applications, and detecting bugs.
AI contributes to the creation of intelligent programs that can learn and adapt
to changing conditions.
Data Science is an interdisciplinary field of
knowledge that is formed at the intersection of three main areas: computer
science, domain expertise, and mathematics with statistics. Its structure can
be explained using a Venn diagram demonstrating the effective interconnection
of these areas. Data Science integrates programming tools, deep domain
knowledge, and mathematical modeling techniques, allowing you to work with data
to analyze, predict, and support decision-making.
Computer science
encompasses programming, modeling, DevOps, and other technologies. Domain
expertise is an examination in any subject area where it is advisable to
analyze data. It can be medicine, advertising, education, sociology, economy,
industry, etc. Good Data Scientists are usually specialists in a particular
domain area. Data Science is based on mathematics and statistics, so people
with an academic or professional background often move into data science, using
their knowledge to analyze data.
Modern AI is the ability of machines and programs to
analyze information, process it, and form conclusions that serve as the basis
for decision-making. The main feature of AI devices is their ability to
constantly learn, quickly process information, accumulate knowledge and apply
it effectively. This means that such systems can acquire the ability to
perceive and analyze the world around them, similar to how the human brain
does. The potential of AI application is too wide, and today it is already used
in many areas, such as medicine, finance, industry, trade, as well as in
everyday human life. By using AI modules, businesses can achieve significant
revenue growth and improved core performance indicators.
The Internet
of Things (IoT) is a system that connects devices connected to the Internet,
ranging from smartphones and TVs to household appliances such as refrigerators.
In fact, any device connected to the Internet that does not belong to
traditional desktop or laptop computers is considered part of the Internet of
Things. smart speakers, etc. That is why machine learning methods form the
basis for the effective functioning of many IoT devices. Machine learning (ML) is a set of methods
in the field of artificial intelligence, as well as algorithms used to create
systems capable of learning from their own
experience. In the process of learning, such systems process and analyze
large arrays of input data, identifying patterns that allow them to perform
complex tasks. Today, machine learning is mainly defined as the direction of development of AI technologies,
aimed at creating flexible algorithms capable of self-learning and adaptation
based on the data obtained. As a subset of artificial intelligence, machine
learning allows computers to "learn" in a similar way to the human
brain. Therefore, this technology is widely used in various fields, including
healthcare, urban planning, financial analysis, and other fields, requiring the processing of large amounts of
data.
For example,
Netflix uses machine learning to analyze data and make recommendations based on
user preferences. In turn, Tesla applies this technology to ensure the
operation of its self-driving cars. Thanks to machine learning, systems are
able to identify patterns in large data sets and make informed decisions based
on this analysis. Machine learning is a subset of AI – one of its components.
Although these concepts are different, they remain closely interrelated.
At the same time, machine learning is one of the areas of AI that provides the ability of a computer system to learn from data and make decisions based on the
results of this learning. In addition to machine learning, the concept of AI
includes such areas as deep learning, robotics, Natural Language Processing (NLP) and other
areas. Unlike conventional programmed algorithms, models machine learning systems improve their
efficiency through the accumulation of experience. Their accuracy depends on the volume and quality of the available
data. In the field of IT, machine
learning is considered as a fundamental area of Data Science. For some time, the terms ML and Data Science have even been
used interchangeably. The Data Scientist profession typically involves knowledge of machine learning, including proficiency in
relevant algorithms, programming languages, and the basics of mathematical
analysis, and statistics. In
general, machine learning has become a massive technology that finds
application in many areas: recommendation engines in social networks, photo
processing algorithms in smartphones, image and natural language recognition
applications, financial fraud monitoring – all this works thanks to machine
learning models.
However, the difference between AI and ML can be
very significant. AI, ML, Big
Data, and the Internet of Things are key technologies that have the potential to radically change
lives and the world. In particular, machine learning and the Internet of
Things have a wide range of applications.
Data Science uses machine analysis methods to
understand the large amounts of data at the disposal of businesses. Today, most
companies accumulate significant amounts of data, but often do not know how to
use it effectively. That is why they involve Data Science specialists and
analysts to optimize processes and gain useful insights.
Closely related to data analysis, the direction
of Data Mining. Data Mining allows you to identify hidden patterns and patterns
in large data sets.
A particularly important process in data
analysis, machine learning, and decision-making is Data Cleaning. This is the
main stage that determines the accuracy, reliability and applied value of the
data used to obtain the results.
Data Exploration is the first important step in
exploratory data analysis, in which existing datasets are examined to identify
patterns, anomalies, or natural structures. The main goal of Data Exploration
is to form a clear story about this data, as well as a deeper understanding of
its structure and features before further analysis or modeling.
Feature engineering is a key step in data analysis,
which involves the process of creating new characteristics (features or
features) that may contain new types of variables or derived data. This
improves the quality of analytical models and allows you to gain deeper
insights from the data. For example, if we have a dataset with indicators of
wages and education levels of a certain population group, you can create a new
variable, which combines these two
indicators. The new variable will allow you to display the dependencies between
indicators in more detail and is used to build analytical models. It is the
creation of such effective variables that is the main task of Feature
Engineering.
Predictive modeling is used to create models based on
available data to predict future events or outcomes. The main goal of
predictive modeling is to select modeling methods, clean up data, build and
adjust the model, and evaluate its performance based on test data. It is used in data science [4; 5; 6; 7; 8; 9],
machine learning and statistics to solve problems where the key is to predict
the values of the target variable.
Using Machine Learning and Data Science,
researchers can predict the structure of a given protein, filter out 99% of ten
thousand possible combinations, and focus on the hundred most likely cases. It
is important to adequately present the data. Personal understanding of data is
one thing, while the ability to interpret and pass it on to others is another,
more difficult task. This process of data transformation and refinement is very
important and costly.
Also, do not confuse the concepts of Data science and Machine learning.
These tools overlap in many ways, but they are still different and have their own tasks. AI is much broader, covering many areas outside of ML. In a broad
sense, AI includes all technologies and methods of imitating human thinking
using machines, including machine and deep learning models, neural
networks, large language models,
generative AI, etc. The implementation of
full-fledged artificial intelligence goes far beyond ML. This requires not only
the construction of data processing algorithms, but also the machine logic of
making judgments and making independent decisions to achieve a certain goal.
Artificial intelligence (AI) is a variety of technological and scientific
solutions. Artifical
narrow Intelligence (ANI) is an algorithm that specializes in one specific task
or area of expertise. This type of intelligence can independently develop or
surpass human intelligence only in the process of performing a specialized
task. Examples of ANI include chess supercomputers, search algorithms,
self-driving car control systems, etc. Machine learning algorithms belong
specifically to the ANI category.
Artifical
general intelligence (AGI) is an AI system that is capable of solving any
intellectual tasks that a human can solve. This type of AI operates with logic
and uses categories of abstract thinking, which allows it to extrapolate its
experience to a wide range of tasks and situations, which ANI algorithms cannot
achieve[11].
Artificial
super-intelligence (ASI) is the highest level of AI that surpasses human
abilities in all aspects, including creativity and social skills.
In addition, AI has become the subject of
philosophical reflections and discussions. At the same time, the field of
practical application of AI tools for the needs of business, education,
medicine, and other industries is developing. This is the main difference
between ML and AI: the concept of Machine Learning indicates practical
technologies and areas of application of algorithms in work tasks. Machine learning is one of the areas of AI where algorithms allow computers to draw
conclusions based on data without following strictly defined rules. That is, computer
systems are
able to detect patterns in complex and multi-parametric tasks (which the
human brain is not able to solve) and find more accurate answers. This leads to
correct forecasting. Neural networks that simulate the work of the human brain
using artificial neurons self-learn based on previous experience and make fewer
and fewer mistakes with each subsequent use [12].
Neural networks are a type of machine learning, not a separate tool. The
main goal of machine learning is to partially
or fully automate decision-making processes in complex analytical problems.
Therefore, first of all, machine learning is aimed at providing
the most accurate predictions based on the input data, so that business owners,
marketers, and other specialists can make informed decisions.
decisions in their work. In the process of learning, a computer system can
predict the result, remember it, reproduce it if necessary, choose the best of
several options. Currently, machine learning covers a wide range of applications from financial institutions, restaurants and gas
stations to production. Machine learning models are
developed to solve practical problems by identifying patterns in large data
sets. These are computer vision tools, advertising algorithms, mobile photo
processing tools, content generation, and
others. At the same time, the main goal of AI technologies is much broader, being to emulate human
intelligence and behavior in various scenarios. ML is just one way to achieve
this goal, but it's not the only one.
Machine
learning is based on three main components that provide the
most effective interaction with the customer:
1. Дані: це основна інформація, яку зазвичай отримують від
клієнта. До них входять будь-які вибірки даних, з якими система повинна
працювати під час навчання.
2. Ознаки: це частина роботи, що проводиться в
тісній співпраці з клієнтом. Визначення ключових бізнес-потреб і спільне
вирішення, які характеристики та властивості повинна відстежувати система в
результаті навчання.
1.
Algorithm: this is the choice
of a method for solving a business problem. This task is solved without the
participation of the client, using the knowledge and experience of the
company's employees.
This
approach allows you to implement machine learning in various areas of business
and technology as efficiently as possible, ensuring the accuracy of forecasting
and the selection of the best solutions.
Data – the more data is uploaded to the system, the better, faster and more
accurate it will work. The data
itself directly depends on the task
facing the
computer system. For example, to recommend products to buyers that
may be of interest to them, they need a history of their previous
purchases. To predict changes in prices in the market, you need a price
history. The most difficult and at the same time the most voluminous part of
the work is the collection of this data. There are two methods of data collection:
manual and automatic. The manual
method is much slower, but more accurate. The automatic method is faster, but can lead to more errors. Qualitative data sampling is very important,
as it affects the accuracy of the prediction obtained as a result. It is also
necessary not to limit data collection to
the limits of human thinking, but to provide the most scattered information, since the system can detect
relationships where a person does not notice them.
Features are properties, metrics, data
characteristics. Properties include those data characteristics on which
the initial result of analysis or modeling directly depends. Since the correct
data characteristics directly affect the result, the process of selecting them
usually takes much longer than the machine learning process itself. it
is the avoidance of limitations in the set of data characteristics so as not to
distort machine perception by personal assumptions. And with it, the end result
[3; 4; 5; 6; 7].
Algorithm is a system of sequential operations to solve a certain
problem. In other words, it is a method of solving a problem. For each specific
task, you can choose a separate sophisticated algorithm. The speed and accuracy
of the input processing result directly depend on the chosen method. There are
cases when even perfectly written algorithms do not help solve business
problems. For example, if you need to increase the number of cross-sales on the
site and you are sure that for this it is enough to improve the algorithm for recommending
products. However, this does not take into account the fact that customers come
through direct search links and ignore the tips for buying other products shown
on the site. Therefore, before starting work, it is important to determine the
real cause of the client's problem. If it is a technical problem, it is solved
by the company's specialists.
According to the presence of a teacher, machine learning can be divided into three
types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
The main types of machine learning:
-
Supervised
Learning is used when it is necessary to train a system to recognize objects or
signals based on labeled data, where the model receives pairs of input data
with correct answers (labels) to adjust its parameters or build a model that
can predict responses for new data.
-
Unsupervised
Learning is based on the principle of "this thing is the same as the
others", which consists in identifying similarities and hidden structures
in unknown data without labels or categories. Algorithms in this type of
learning can detect differences, find anomalies, and recognize unusual patterns
without using labels that indicate correct answers.
-
Reinforcement
Learning consists in the interaction of the agent's model with the environment,
where he acts in order to maximize the rewards received for his actions. The
difference between this approach is that it does not have unambiguous correct
solutions, since the results depend on the variable reward. Reinforcement
Learning is used where the computer system is faced with the task of
effectively completing tasks in an external environment, where there are many
possible options for action, such as in computer games, trading operations and
unmanned vehicle management.
According to the use of algorithms, there
are two main
types: deterministic and probabilistic.
A deterministic algorithm is a
computational process that always produces the same result under the same
inputs and execution conditions. Determinism ensures reliability and
predictability of results, which is important in many fields, including
programming, science, and engineering, where critical accuracy and stability of
task execution matter.
A probabilistic algorithm is a
computational process that uses elements of randomness or probability
distributions to solve problems. These elements can change their result under
the same input conditions, even with different runs. Such algorithms are used
in statistics, machine learning, finance, and other fields to simulate complex
phenomena and solve problems where randomness or uncertainty is important.
Classical learning is a well-known
and well-studied learning algorithm that was developed more than 50 years ago,
mainly for statistical bureaus. These algorithms are mainly applied to work
with data such as classification, clustering, regression, and other analytical
tasks. They are used for forecasting, customer segmentation, and other aspects
of data analysis and processing.
Neural networks
and deep learning represent a modern approach to machine learning, applied in
cases where recognition or generation of images and videos, complex control or
decision-making algorithms, machine translation, and similar tasks are
required. Yes, multiple approaches can be combined, in which case multiple
independent machine learning models are combined together as ensembles of
models to achieve better results.
Therefore, ML technologies can be
categorized as artificial intelligence, and in many contexts, these concepts
are used interchangeably. However, we will focus on their differences and
determine how AI differs from ML. Full-fledged artificial intelligence is
able to solve a wide range of tasks that can be solved by humans. AI is able to
reproduce the patterns of thinking that humans are used to calling common
sense, extrapolate the experience gained to a wide range of new tasks, identify
abstract thinking and creativity, and take into account the social and ethical
context when making decisions. ML has a narrow application related to data processing
to solve specific problems. The benefits of combining machine learning and the
Internet of Things are numerous. One of the main advantages of machine learning
is its ability to process huge amounts of data and extract insights from it,
even if it is not known exactly what trends to look for. which helps to reveal the essence of the
problem. Machine learning algorithms typically use one or more data libraries
in order to speed up the development process. By definition, machine
learning is an integral part of artificial intelligence, they are different but
closely related.
As a result of
transformational processes in the institutional economy of Society 5.0, there
is an intellectualization of the economy with signs of innovation and
digitalization at all levels. In the context of globalization, the substitution
of labor for knowledge is a radical change of the XXI century, characterized by
the use of the latter in the practical processing of knowledge resources.
In this regard, in the
digital Society 5.0, the source of value is knowledge, not labor. Therefore, when implementing digitalization in
the environmental sphere, an extremely important aspect is to take into account
side effects, such as cyber incidents and cyberattacks, as well as to ensure
effective cybersecurity and protection of the created digital space from
cybercrime and possible abuse of digital technologies.
In the context of the rapid
development of artificial intelligence, it is particularly important to
establish a legal framework that can ensure a balance between technological
innovation, ethical responsibility and the protection of fundamental human
rights. In Ukraine, as in many other countries, a systematic legislative
framework that comprehensively covers the regulation of the development,
implementation and monitoring of AI systems has not yet been created. Existing
regulations, particularly in the area of personal data protection, are only
partially capable of addressing the specifics of algorithmic decision-making,
the absence of human control, or the functioning of self-learning systems.
One of the key legal challenges is
determining responsibility for the consequences of decisions made by AI
systems, especially in cases of autonomous operation. There is a need to
introduce principles of transparency (explainability) and reproducibility of
decisions, as well as to develop standards for mandatory auditing of algorithms
used in the public sector or in vulnerable social environments, such as healthcare,
the judiciary, and social protection. The legislative initiative should provide
for the creation of an independent body for ethical monitoring of AI use (e.g.,
an Algorithm Ethics Commission), similar to the institutions proposed under the
EU AI Act.
In the law enforcement sphere, there
are difficulties in qualifying violations committed through AI, especially when
it comes to so-called ‘digital harm’ that has no direct material expression but
violates privacy, the right to a fair trial, or equality. For example,
discrimination caused by opaque algorithms for selecting job candidates or
granting loans requires the development of legal protection mechanisms at the
level of individual court appeals, administrative appeals, or the introduction
of ‘algorithmic ombudsman’ models.
On the other hand, legal regulation
of AI is impossible without taking ethical dimensions into account. That is why
there is a growing need to create ethical frameworks based on universally
recognised principles: respect for personal autonomy, fairness, inclusion,
avoidance of harm, integrity and accountability. International standards play
an important role here, in particular the UNESCO Recommendation on the Ethics
of Artificial Intelligence (2021), which can be implemented in national
policies through the development of ethical codes for developers, suppliers and
users of AI systems.
Particular attention should be paid
to the problem of ‘digital inequality’ caused by unequal access to data,
algorithms and opportunities to use AI in the public and private sectors. If
these imbalances are not addressed through legal and political means, a
situation may arise where the digital elite gains advantages in knowledge,
power mechanisms and resources, which would contradict the principles of
democracy and social justice.
Thus, the formation of a national
and cross-border legal regime for AI regulation requires a systematic approach
that integrates institutional, ethical, regulatory and educational components.
The decisive factor in the effectiveness of such a regime should be not only
the number of laws, but also the quality of institutional accountability, the
existence of mechanisms to monitor compliance with ethical standards and human
rights, and the constant updating of the regulatory framework in line with the
pace of technological progress. Only under such conditions will artificial
intelligence serve as an instrument of public good rather than a source of
uncontrolled risks.
Conclusions.
AI is one of the priority areas in the field of
digital transformation of public administration, so it is necessary to quickly
make decisions that will allow you to adapt to the rapidly changing processes
in the field of AI and influence the evolution of technologies. To ensure the
effective implementation of AI technologies and obtain the most positive
results, it is necessary to constantly develop and update complexes of optimal
regulatory measures. It is also important to focus not only on the development
of laws, programs, strategies, but also to take into account the problems that
hinder and inhibit the development of AI. The interaction of computer science
and AI determines a new level of development of modern society, turning it into
a technologically oriented and intellectually rich space. The application of
these two fields in various fields, from public administration and health care
to economics and education, highlights their importance in addressing today's
challenges and challenges. Social informatics, which is based on the analysis of
social processes and the use of information technologies, interacts with
artificial intelligence, which provides the ability to efficiently process and
interpret large amounts of data. This cooperation is manifested in the
implementation of e-governance, the improvement of medical technologies, the
creation of intelligent economic management systems and the improvement of
educational practices. At the same time, an important aspect of the application
of these technologies is to take into account the ethical and social challenges
that may arise as a result of automation and the increase in the processing of
personal data. The balanced development of social informatics and artificial
intelligence requires careful consideration and protection of the rights and
interests of citizens. In general, this interaction is formed around the idea
of using advanced technologies to improve the quality of life, management
efficiency and solve global problems. The development of these industries can
determine not only the technological, but also the socio-cultural picture of
the future, in which information technologies and intelligent systems become a
necessary part of a sustainable and modern society. The need to introduce
digitalization in the regulation of environmental relations can also be
considered in the context of individual industries.
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