Authors

  • Kamoliddin Gulmurodov
  • Malika Akbarova
    Tashkent State University of Economics

DOI:

https://doi.org/10.71337/inlibrary.uz.ijai.114461

Abstract

This article discusses the development of the Uzbek economy, labor migration, causes, consequences, problems of labor migration, employment problems, negative and positive aspects of migration, the most important economic and social causes of unemployment, ie unemployment, Finding suitable jobs, obtaining higher and secondary special education, ie studying and other types of problems are studied and scientifically based proposals are given to solve the problems.

 

 

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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE

ISSN: 2692-5206, Impact Factor: 12,23

American Academic publishers, volume 05, issue 06,2025

Journal:

https://www.academicpublishers.org/journals/index.php/ijai

page 137

THE USE OF DIGITAL TOOLS IN THE MANAGEMENT OF

MIGRATION PROCESSES

Gulmurodov Kamoliddin Abdukodir ugli

Tashkent State University of Economics

ORCID:

0009-0009-1936-7885

e-mail:

k.gulmurodov@tsue.uz

Akbarova Malika Ilkhomovna

Tashkent State University of Economics

ORCID:

0009-0006-9778-2636

e-mail:

m.akbarova@tsue.uz

Annotation.

This article discusses the development of the Uzbek economy, labor migration,

causes, consequences, problems of labor migration, employment problems, negative and

positive aspects of migration, the most important economic and social causes of unemployment,

ie unemployment, Finding suitable jobs, obtaining higher and secondary special education, ie

studying and other types of problems are studied and scientifically based proposals are given to

solve the problems.

Key words.

Migration, emigration, immigration, labor market, rural and urban population,

family, women, migration balance, title population, region, population, seasonal work,

insurance, infrastructure.

Introduction

"Digital economy" is an activity directly related to e-commerce, which includes: proposals for

the provision of online services, online stores, information sites making money from advertising,

online trading, etc. If so, then almost any method of making money on the Internet can be

classified as a digital economy[1].

On the other hand," digital economy " is an economy based on new methods of data creation,

processing, storage, transmission, in addition to digital computer technologies. The main

technologies of the digital economy are big data (data itself and methods of working with them),

artificial intelligence, blockchain technology, fog computing, quantum technologies, robotics,

virtual reality, etc.

Globalization and the digitalization of economic processes in the world have significantly

influenced the mobility of the population and, in particular, labor migration. According to

various estimates by the world research community, there are more than 200 million inter-

country immigrants worldwide [1].

At the present stage, the process of adaptation and integration of immigrants requires who

provides public services for their adoption, registration, documentation and constantly

improving and creating new forms. In order to carry out these tasks, it is very important to

obtain clear concepts about problem situations in which the interaction with migrants occurs.

Human capital is an intensive factor in the development of the digital economy. Under the

influence of rapid digitalization, the essence of Labor is changing. This transformation is not

purely technical, but also takes place digitally in the way that political, social and legal changes

and accompany them. Artificial intelligence, big data, robotization and automation, along with

globalization and demographic changes, are driving the digital transformation of labor markets.

The ongoing changes in the labor market are also creating unprecedented opportunities, which


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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE

ISSN: 2692-5206, Impact Factor: 12,23

American Academic publishers, volume 05, issue 06,2025

Journal:

https://www.academicpublishers.org/journals/index.php/ijai

page 138

require the reflection and development of influential policies that increase stability and

flexibility in different areas: professional development, social policy, etc [1].

One of the big changes in the employment of the population over the past decade is the

emergence of online digital labor platforms. The growth of the economy mediated by internet

browsers, online platforms has become a new direction in the field of employment of labor

resources. The " giant online platform economy "is a new labor market where"employers use

online labor platforms to engage workers in partial, short-term, or project work over the

Internet". Work online is formed in a mediation relationship that involves at least three

participants: an online platform, an employee and a client. The platform works as an online

business that facilitates commercial communication between at least two parties - workers and

employers-by mediating these relationships.

Literature Review

Constantly emerging and increasingly competing with recruiting agencies, the platforms work

to serve different markets. These include platforms Fiverr, Freelancer, Upwork, Outsourcely,

Guru, Peopleperhour and Mechanical Turk. They represent a new way of organizing labor and

offering services. Global platforms, as noted, have a transnational character. Today, there are

more than 2,000 platforms in the world that provide different forms of digital labor [2].

However, this is not an exact number, since it is difficult to accurately determine the number of

platforms currently operating in the world. For example, in Europe, the European Commission

estimates that there are 273 platforms in nine countries, while another source estimates 300 in

France[2]. Large discrepancies in the numbers lead to the differentiation of the collected data

and different conclusions.

Migration management refers to the accounting of migration flows in an orderly and predictable

way, the creation of a basis for regulation, the various strategies, concepts and processes agreed

and adopted by the respective entities in the migration relationship.

Migration management is a controversial concept. The political will and ability to perceive to

control migration flows often contradicts reality, since migration is a complex multilevel

phenomenon that cannot be easily controlled.

Government agencies can use digital technology to verify identity, enforce border security and

control, as well as analyze data on visas and asylum seekers, include and register migrants into

the migration account, and issue long-term documents[3]. All of these processes are usually

long-lasting, mostly with manual registration based on claims from immigrants and asylum

seekers. Digital transformation can radically change the government's approach to managing

international migration. This has already become a reality in some countries. For example,

Canada uses algorithmic decision-making in the definition of Immigration and asylum[4].

Switzerland is currently testing an algorithm to improve refugee integration[5]. In the European

Union (later referred to as EU), the Schengen Information System helps facilitate the return of

migrants to their countries of origin for Face Recognition, DNA and biometric data[6]. The

German federal Office for Migration and refugees (Bamdesamt fur migration und Flüchtlinge,

BAMF) tested technologies such as automatic facial and dialectal recognition, name

transliteration, and mobile device analysis for identity validation[7]. With the help of digital

technologies, applications, personal data of migrants, countries of origin are checked.

Research Methodology

In the course of scientific research, the article presents scientifically based proposals on

migration of the population, its importance in scientific observation, using methods based on


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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE

ISSN: 2692-5206, Impact Factor: 12,23

American Academic publishers, volume 05, issue 06,2025

Journal:

https://www.academicpublishers.org/journals/index.php/ijai

page 139

abstract-logical, systematic analysis, analysis and synthesis principles, and using analytical

analysis, comparative analysis, statistical data analysis as solutions to problems.

Analysis аnd results

All of these processes previously required longer examination and evaluation of specialists,

which now take a few minutes[8]. Recently, the European Union has passed new laws aimed at

the use of artificial intelligence and related technologies in the field of migration and security[9].

In the same way, governments are exploring the possibility of using digital transformational

technologies to predict the next "migration crisis". For example, the Swedish government used

"migration algorithms"based on an automated program to predict future migration flows[10].

China is actively using digital technologies in migration management, mastering digital

technologies based on data from the social credit system.

These examples demonstrate well the tendency to use new digital technologies to manage

international migration and ensure border security.

1- fig. Indicators of the employment of migrants participating in the survey in what area

The picture shows that the migrant is going to other countries and is becoming unofficially busy

(22.1%). This can lead to a number of mammoths: labor law, labor security, who does not

provide for Hecht when working conditions are unofficially occupied, and as a result, the

possibility of non-specific events arises.

An analysis of the reasons for informal employment and whether or not they worked in the

official workplace was found in an analysis that found that 25.0% of participants were


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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE

ISSN: 2692-5206, Impact Factor: 12,23

American Academic publishers, volume 05, issue 06,2025

Journal:

https://www.academicpublishers.org/journals/index.php/ijai

page 140

dissatisfied with the low monthly wage and therefore migrated, and that 20.1% of those with

secondary-specific education did not work in the official workplace at all.

So, when creating vacancies in our country, we found that the amount of monthly wages should

also be taken into account, and the monthly wage is low-set, depending on the economic

situation of the territory.

The employed part of informal domestic migrants is almost 15.2% of 2-2.5 million. it has been

cited around them that they receive monthly wages and that working conditions are unsecured

(8.7%). In provinces, the amount of monthly may be even lower. Therefore, the percentage of

migration in the population is increasing.

Taking the indicator that migrants also use digital tools in the current informed time, it gives the

following result (1-table)

1 - table

The extent to which migrants use digital technology

Looking for a job using digital technology

(computer, phone, tablet)?

Number Number

in %

Xa

219

42,607

No

245

47,665

I do not use digital technologies

50

9,7276

The survey showed that 9.7% of participants could not use it at all-this may be due to their age

indicator. The proportion of people aged 40-49 and 50-60 in the survey was 13.6% and 3.7%

respectively. This age can be justified by the fact that instead of searching online for

information about job vacancies in labor resources, they use the traditional method a lot, that is,

they are ordered to take certain about themselves and throw them into organizations that are

looking for work.

Figure 2 shows that if migrants are offered a job in the specialty first, so that they can move

from regions with a large population, to regions with a low population, then 207 people-28.2%,

and secondly, if the salary is enough to support the family, then 191 people-26.0%, and thirdly,

if the conditions for family migration are 144 people-19.6 %


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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE

ISSN: 2692-5206, Impact Factor: 12,23

American Academic publishers, volume 05, issue 06,2025

Journal:

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page 141

2 - fig. Ensuring employment through internal migration

But now the proportion of citizens in our country who wish to go abroad is 356 people, which

is 69.2%. Part of the higher education citizens who participated in the survey can also see that

they agree to leave for external labor migration. Those who have expressed a desire to leave

without a higher education.

Of those surveyed, 235 showed that 45.7% were labour migrants in foreign countries, while 237,

46%, were domestic migrants, claiming formal or informal employment in the capital. So the

city of Tashkent for domestic migrants is considered on the example of a city where work can

be found.

Digitization in the field of migration develops differently in different countries. Digital

transformation among developed countries, such changes can strengthen their leading position

and will be one of the main factors in managing risks associated with migration processes. In

developing countries that do not have the opportunity to actively use digital technologies, on

the contrary, the delay in their development increases, and in general, this situation affects the

economic development of developing countries. The consequences of rapidly developing digital

technologies for developing countries can be an increase in the gap between countries with poor

Internet connectivity and countries with very high levels of digitization.

List of literature used:

1. Kässi O., Lehdonvirta V. Online Labour Index: Measuring the Online Gig Economy for

Policy and Research. MPRA Paper No. 74943. Oxford Internet Institute. 03.11.2016. 19 p.

2. Глущенко Г. И. Развитие виртуальной миграции в контексте цифровизации //

ДЕМИС. Демографические исследования. 2021. Т. 1. № 2. С. 57–64. DOI:

10.19181/demis.2021.1.2.4

3. Chui M. [et al]. (2018). Notes from the AI frontier. Applying AI for social good.

Washington D.C.: McKinsey Global Institute. 52 p.

4. Molnar P., Gill L. (2018). Bots at the gate: a human rights analysis of automated

decision-making in Canada’s immigration and refugee system. Toronto: University of

Toronto. 18 p.


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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE

ISSN: 2692-5206, Impact Factor: 12,23

American Academic publishers, volume 05, issue 06,2025

Journal:

https://www.academicpublishers.org/journals/index.php/ijai

page 142

5. Bansak K., Ferwerda J., Hainmueller J., Dillon J., Hangartner A., Lawrence D.,

Weinstein J. (2018). Improving refugee integration through data-driven

algorithmic assignment // Science. V. 359. I. 6373. Pp. 325–329. DOI:

10.1126/science.

6. Regulation 2018/1860/EU on the use of the Schengen Information System for the return of

illegally

staying

third-country

nationals

OJ

L312.

Режим

доступа:

https://www.europeansources.info (дата обращения: 20.08.2020).

7. Tangermann J. (2017). Documenting and establishing identity in the migration

process. Challenges and practices in the German context. Nuremberg: Federal Office

for Migration andRefugees. 65 p.

8. Patrick P.L., Schmid M.S., Zwaan K. (2019). Language analysis for the determination

of origin. Current perspectives and new directions. Cham: Springer. 271 p. DOI:

10.1007/978-3-319-79003-9_1.

9. Regulation 2019/816/EU establishing a centralised system for the identification of Member

States holding conviction information on third- country nationals and stateless persons

(ECRIS-TCN) to supplement the European Criminal Records Information System and

amending Regulation OJ L135. Режим доступа: https://www.europeansources.info/record.

(датаобращения: 20.08.2020).

10. Carammia M., Dumont J.-C. (2018). Can we anticipate future migration flows? Paris:

OECD. Pp. 1–9.

References

Kässi O., Lehdonvirta V. Online Labour Index: Measuring the Online Gig Economy for Policy and Research. MPRA Paper No. 74943. Oxford Internet Institute. 03.11.2016. 19 p.

Глущенко Г. И. Развитие виртуальной миграции в контексте цифровизации // ДЕМИС. Демографические исследования. 2021. Т. 1. № 2. С. 57–64. DOI: 10.19181/demis.2021.1.2.4

Chui M. [et al]. (2018). Notes from the AI frontier. Applying AI for social good. Washington D.C.: McKinsey Global Institute. 52 p.

Molnar P., Gill L. (2018). Bots at the gate: a human rights analysis of automated decision-making in Canada’s immigration and refugee system. Toronto: University of Toronto. 18 p.

Bansak K., Ferwerda J., Hainmueller J., Dillon J., Hangartner A., Lawrence D., Weinstein J. (2018). Improving refugee integration through data-driven algorithmic assignment // Science. V. 359. I. 6373. Pp. 325–329. DOI: 10.1126/science.

Regulation 2018/1860/EU on the use of the Schengen Information System for the return of illegally staying third-country nationals OJ L312. Режим доступа: https://www.europeansources.info (дата обращения: 20.08.2020).

Tangermann J. (2017). Documenting and establishing identity in the migration process. Challenges and practices in the German context. Nuremberg: Federal Office for Migration and Refugees. 65 p.

Patrick P.L., Schmid M.S., Zwaan K. (2019). Language analysis for the determination of origin. Current perspectives and new directions. Cham: Springer. 271 p. DOI: 10.1007/978-3-319- 79003-9_1.

Regulation 2019/816/EU establishing a centralised system for the identification of Member States holding conviction information on third- country nationals and stateless persons (ECRIS-TCN) to supplement the European Criminal Records Information System and amending Regulation OJ L135. Режим доступа: https://www.europeansources.info/record. (дата обращения: 20.08.2020).

Carammia M., Dumont J.-C. (2018). Can we anticipate future migration flows? Paris: OECD. Pp. 1–9.