Artificial Intelligence Regulation in the Areas of Data Protection, Information Security, and Anti-discrimination in Western Balkan Economies

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

Western Balkan (WB) economies are lagging behind more advanced post-transitional countries in terms of economic development and the quality of the institutional framework and public services. As a response to the rigidity in the business environment and high taxation, private entrepreneurs shift part of their activities into the unofficial sector. We aim to estimate the volume of the unofficial economy (UE) in seven WB economies: Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, Kosovo and North Macedonia. In providing reliable estimates of the range of the UE, we applied two different methods: multiple indicators, multiple causes (MIMIC) and a direct survey of 21,000 individuals. The highest size of the UE, based on the average of both methods, is estimated for Montenegro. Slovenia, the most developed economy included in the sample, has the lowest share of the UE. The direct survey provides results on the propensity of different socioeconomic groups to engage in unofficial activities. The improvement of the institutional environment, quality of public services and lower tax burden are expected to be more effective than repression measures in the combat against the UE.

The political environment plays a crucial role in steering societies to address the challenges posed by global warming and contributing to global initiatives toward a resilient and environmentally sustainable future. Political regimes in different places in the democracy-authoritarianism spectrum show varying degrees of effort to reduce their nation's respective carbon emissions. This paper aims to determine the role of political regimes along with governance dimensions in climate change mitigation. The analysis uses the V-Dem database and the World Governance Indicators (WGI) to conceptualise and measure each country's political system and its institutional quality of governance, respectively. The empirical model uses a panel database of the variables of interest for the years 1998-2022 regarding the Western Balkan (WB) economies. The target group of countries sparks our interest due to their significant challenges, such as widespread pollution, political regime changes, and severe governance problems. Furthermore, integrating the Environmental Kuznets Curve (EKC) hypothesis, which suggests a complex relationship between economic growth and environmental sustainability, the analysis provides evidence that the relationship between CO2 emissions and GDP per capita follows a quadratic trend, suggesting that as GDP per capita increases, the associated CO2 emissions may not change linearly but instead follow a parabolic pattern. WB countries also share the ambition of EU accession and hold the EU candidate status. To the extent these states manage to overcome their institutional issues, they will be in the position to implement the EU environmental acquis efficiently, which will amount to a critical step in the region's environmental health. Policymakers and scholars of the field will benefit from this paper since it determines the impact of political systems precisely matched with empirical results to derive conclusive answers regarding the political systems and climate change debate.

By 2024, Albania’s food and non-alcoholic beverage prices reached 100% of the EU average, up from 68% in 2012, despite a nearly 30% ‎appreciation of the local currency. This was the sharpest increase in food prices recorded in the Balkans during the period, despite the country recording similar growth rates to the regional average. Food prices in Albania are today at levels higher than in countries such as the ‎Netherlands and Spain, which have significantly higher GDP per capita and household wellbeing than Albania (GDP per capita at 35% of ‎the EU average, second from last in the Balkans). In 2024, Albanian families, who allocate nearly 40% of their income to food, face price levels comparable to those of much wealthier EU households. This study provides a robust, regression-based comparative analysis of food price inflation ‎in Albania from 2013 to 2024, benchmarking trends against the European Union (EU), key EU trade partners (Italy, Greece, Germany), and ‎Western Balkan economies. It compares food inflation dynamics across two periods, pre-COVID (2013–2021) and post-COVID (2022–‎‎2024), and evaluates the role of currency appreciation in moderating domestic price pressures. Using harmonized consumer price data and ‎exchange-rate-adjusted regression models, the study reveals that Albania’s food prices have risen earlier, faster and more persistently than in ‎its peer countries, even as the Albanian lek appreciated nearly 30% over the decade. While food price movements in Albania appear to align ‎with EU trends, statistical modeling shows a significantly stronger elasticity and an amplified domestic response, particularly before 2021. ‎Once exchange rate effects are accounted for, Albania’s correlation with EU food prices collapses, unlike in other Western Balkan countries ‎or EU trade partners, indicating that FX gains were not passed on to consumers. These findings provide strong empirical evidence that Albania’s inflation outcomes are not simply imported but are domestically amplified due to weak market competition, non-transparent pricing, and limited regulatory oversight. The paper concludes that internal structural issues have muted the pass-through benefits of currency appre‎ciation to consumers, while price increases closely followed the trend observed in the EU. This underscores the need for stronger competi‎tion policy and more inclusive market mechanisms to ensure that macroeconomic gains support household welfare. The paper is especially ‎relevant to policymakers, central banks, international institutions, and researchers seeking to understand how structural inefficiencies can ‎offset macroeconomic advantages in small economies‎.

The world economic crisis that paralyzed the world economy in 2008 and 2009 had a profound impact on all countries in the world. Due to the interconnectedness of national economies the crisis spread rapidly from its centre in the United States to the world. There were two main transmission channels for the spread of the crisis between countries - international trade and the exchange of private capital between states in the form of foreign direct investment (FDI). This economic downturn has greatly influenced the domestic economic stability of the Western Balkan economies. The Western Balkan countries have shaped their economic policy towards European Union (EU) membership, resulting in a high degree of liberalization in international economic relations accompanied by a commitment to free international capital movement. Since this region has close economic ties with the EU the crisis spread to the region very quickly, manifesting itself in decreasing regional exports to the EU market and a downward trend of FDI inflow to the region. This paper will focus on the impact of the world economic crisis on the Western Balkan economies and especially on their exports and FDI inflow. Our empirical analysis, based on panel data, uses a wider sample of Central and Eastern European Countries (CEEC) which includes the Western Balkans, since we wanted to analyze if the effects of the economic crisis in the Western Balkans are specific or are common to most countries in transition. The analysis shows that Western Balkan exports have suffered due to the crisis, but reveals some interesting results on the different dynamics of export flows which depend on regional trade integration for their destination.

Aim . The presented study aims to determine the impact of artificial intelligence as a modern breakthrough technology on productivity, to explore how the implementation of artificial intelligence technology will affect the preservation of jobs in different industries, what opportunities it will create for business in terms of increasing productivity along the entire value chain, and how this will affect GDP growth and key economic indicators in various countries. Tasks . The authors identify priority directions for the development and implementation of artificial intelligence in various economic sectors; analyze econometric results obtained during previous studies; substantiate the advantages and opportunities of artificial intelligence to facilitate its implementation in the business processes of organizations. Methods . This study uses the methods of analysis, systematization, and correlation analysis. Results . Various definitions of artificial intelligence, levels of its functionality, and fields of application are analyzed. The ways and prospects of using artificial intelligence in different countries are examined regressively by industry and geographical region, with an emphasis on the ways of using artificial intelligence systems (wired/special and adaptive) and automation technologies in the implementation of artificial intelligence. The potential effects of artificial intelligence at each stage of the company's value chain are described. Examples from different industrial sectors are provided. Based on the correlation analysis, the relationship between the implementation of artificial intelligence and productivity growth is presented. Conclusions . Implementation of artificial intelligence has a global economic impact on key economic indicators such as employment and GDP, which is especially important in the current crisis situation. The effect of artificial intelligence should be enough to maintain the rate of economic growth in the long term. The direct impact of artificial intelligence on GDP is due to increased income and employment in firms and industries engaged in the development or production of artificial intelligence technologies. Secondary (indirect) effects will come from other sectors that use certain artificial intelligence technologies to increase the efficiency of their processes and solutions and improve the accessibility of information. Regions implementing an artificial intelligence technology of higher quality can expect its impact on labor productivity to be even more significant.

The rapid expansion of Business Process Outsourcing (BPO) has transformed the global services economy, and Albania is emerging as a competitive nearshoring destination in the Western Balkans. This study examines the intersection of BPO and digitalisation in Albania, exploring how technological innovation, artificial intelligence (AI), and cloud-based automation are reshaping service delivery, labour productivity, and competitiveness. This study is explicitly framed as a desk-based policy and analytical study relying exclusively on secondary data, without primary data collection based on secondary data from OECD, World Bank, IBM, and European Commission reports (2023–2025). Findings indicate that Albania’s BPO sector benefits from low labour costs, multilingual human capital, and favourable fiscal policies, yet faces challenges related to technological capability, digital infrastructure, and talent retention. Through a PEST analytical framework, this study identifies the macro-environmental factors influencing BPO development and proposes strategic directions for enhancing digital readiness and regional integration. The study further expands its comparative analysis to include other Western Balkan economies, Kosovo, North Macedonia, and Montenegro, providing a broader perspective on Albania’s position within the regional outsourcing ecosystem. This research contributes to the academic and policy discourse on digital transformation by presenting an integrated model aligning BPO growth with sustainable innovation and regional competitiveness.

The aim of this study is to evaluate the impacts of the coronavirus pandemic on undeclared work in the Western Balkans and the emergent challenges for state authorities, and policy responses, in the immediate and longer-term in relation to tackling undeclared work. To achieve this, section 2 briefly evaluates COVID-19s impact on undeclared work in the Western Balkans. This examines its impacts on different types of undeclared enterprise and worker and the size and characteristics of those affected in the six Western Balkan economies using data from the Employment and Social Affairs Platform (ESAP1) economy reports on undeclared work and the 2020 Balkan Barometer. Section 3 then outlines the challenges state authorities in the Western Balkans and beyond witnessed in adapting to confinement in terms of on the one hand, providing protection for staff and on the other hand, their adoption of new working methods and practices. To do this, information provided at the 1st Plenary meeting of the Western Balkan Network Tackling Undeclared Work as well as from other European enforcement authorities is used. Section 4 turns to two important immediate challenges facing Western Balkan economies. On the one hand, there is the potential abuse of the short-term financial support schemes in Western Balkan economies, especially in relation to undeclared work, and on the other hand, the issue of protecting vulnerable workers in the undeclared economy left without revenue during the confinement period and possible policy responses that have and could be adopted. Section 5 then turns to three future challenges in the recovery period. The first issue is whether the conventional policy approaches and measures for transforming undeclared work into declared work will continue to remain valid. The second issue is whether the same or different policies will be required for preventing declared work shifting into the declared economy and the third and finally, there is the future challenge regarding the withdrawal of the short-term financial support packages and its implications for the growth of undeclared work. The sixth and final section then draws conclusions and makes recommendations.

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

This article updates the authors’ previous analysis of the economic position facing western Balkans countries. It analyses development between 2008 and 2018 in five countries from the region - Serbia; Bosnia and Herzegovina; Montenegro; North Macedonia; and Albania - and contrasts this with the three newest EU member states - Bulgaria; Romania; and Croatia. Its argumentation is based on the following parameters: GDP growth; GDP per capita; GDP at purchasing power parity; the share of investment in GDP; average monthly net wages; average monthly gross wages; inflation; the share of foreign and public debt share as a percentage of GDP; and balance of payments. In 2018, economic growth in the western Balkans reached 3.8 per cent, supported by increased public spending and, in Albania and North Macedonia, also by a rise in net exports. According to World Bank projections, western Balkans economies are projected to continue to expand in 2019-20, but this stable outlook is vulnerable and subject to risks, including not least the migration of well-educated and highly-skilled young people.

The relevance of the paper is due to the digitalization of the economy and the introduction of artificial intelligence in production processes. This paper attempts to assess the effectiveness of artificial intelligence for the automation of production. Thus, the purpose of the work is to evaluate the effect of the introduction of artificial intelligence into automated process control systems. For this, an algorithm for implementing artificial intelligence was developed, i.e., procedures and their sequence were identified when implementing artificial intelligence in automated process control systems. The following procedures were considered: selection of implemented artificial intelligence functions, selection of an artificial intelligence system, selection of hardware implementation and acquisition of artificial intelligence, formation of tests for artificial intelligence training, implementation of artificial intelligence, and evaluation of results of implementing artificial intelligence. When implementing artificial intelligence, one should choose artificial intelligence based on neural networks with deep learning. The ambiguity of the cost estimate existed when selecting hardware due to the lack of data from developed artificial intelligence versions. This complicates the definition of capital expenditures. A formula for calculating costs of implementing artificial intelligence costs in automated process control systems is proposed. The introduction of artificial intelligence into an automated process control system will not provide significant savings. Such conclusions are drawn on the basis of the calculation method.KeywordsArtificial intelligenceAutomated process control systemEconomic efficiency

Although Western Balkan economies are still on the list of EU candidates or potential candidate countries, they do have some integration with EU economies. The EU is the leading trading partner of the Western Balkans and is one of the top destinations for the fl ow of people. The Western Balkan economies have been trying to address their populations’ ongoing emigration (the so-called “brain drain” phenomenon) by implementing targeted economic policies. Since one of the main contributors to economic growth is the ability to create innovation, it is crucial to building innovation potential. The main challenge for innovation policy is to provide a favourable environment for entrepreneurship and economic growth to create jobs. Thus, this paper aims to examine innovation potential – an innovation input – as it creates the conditions needed for innovation development. The results of analysing the data (on research and development spending, human resources, an environment friendly to innovation, and intellectual property rights) indicate that the Western Balkan countries are lagging behind the EU in many aspects of innovation potential, so they should still develop their strategies towards creating higher innovation potential. Thus, they will be able to have a higher level of innovation and, as a result, be more competitive in economic terms.

The paper explores the impact of foreign direct investment (FDI) on the economies of the Western Balkans during their transition to a market system. The paper recalls the political and historical circumstances that have delayed transition in the Western Balkans economies, and draws attention to the specific features of FDI that have influenced their economic development. The main hypotheses are formulated and basic tests performed on data from the manufacturing sector. However, data limitations mean that we can only test for horizontal, rather than vertical, spillovers and in practice we are not able to identify many significant horizontal spillover effects. This finding can probably be explained by various factors – institutional, economic and political – that have constrained FDI effects in the Western Balkan economies in comparison with the Central East European countries. Our work has important policy implications; in order to accelerate economic development, Western Balkan policymakers may need to implement more effective economic policies.

The regional integration with the CEFTA significantly influenced the liberalisation of the market, which led to an increase in exports of agri-food products in the Western Balkan economies. The main objective of this paper is to examine the impact of the CEFTA on the export of agrifood products of Western Balkan economies on the global and regional markets. In this context, comparative advantages have been analysed, and the gravity model based on panel data has been estimated. According to the results, all the Western Balkan economies have comparative advantages in exporting on the international market. Results of the gravity model estimation showed that free trade agreements with the CEFTA positively affected the intensification of agri-food product exports. Western Balkan economies have similar economic development and competitiveness levels. Thus, reintegrating the market established by CEFTA affected the export of agri-food products.

Background/Purpose: The agriculture sector is the backbone of every nation which contributes to the global economy. The implementation of technology in agriculture has brought revolutionary development in its outcome. Due to this, a drastic improvement in the global economy from the agricultural sector is expected. Moreover, the implementation of artificial intelligence (AI) improves the productivity of farmers giving solutions to various challenges faced by the farmers. The various AI tools that are developed for the agriculture sector include precision farming, predictive analytics, automated machinery, smart irrigation systems, crop and soil monitoring, supply chain optimization, weather forecasting, and livestock management. Adopting AI in agriculture faces several challenges despite its long-term benefits. The high upfront costs to be invested in implementing AI technology make it difficult for small-scale and developing farmers to invest in AI. Implementing the above technology needs technical skills, fast internet connectivity, and costlier equipment. Due to the lack of the above-mentioned requirements, the AI technologies that are meant for agriculture do not reach the farmers. This results in the wastage of resources for AI without the outcome. Considering the above issues an appropriate simplified model is proposed that facilitates the adaptation of the AI technology by small and medium-scale farmers in their agriculture to improve the performance. Objective: The objective of this paper is to review the various journals related to the implementation of AI in Agriculture and to study the various issues related to its implementation. It also aims at identifying the research gap which will help to develop a model suitable for the end like small-scale and medium-scale farmers. Design/Methodology/Approach: A systematic literature review was conducted by gathering and examining relevant literature from international and national journals, conferences, databases, and other resources accessed via Google Scholar and various search engines. Findings/Result: The agriculture sector, crucial to every nation's economy, has seen revolutionary advancements through technology, especially AI. AI tools like precision farming, predictive analytics, and smart irrigation promise to enhance productivity and address various agricultural challenges. However, high implementation costs, resistance to new technologies, and lack of necessary infrastructure hinder widespread adoption among small-scale and developing farmers. To overcome these obstacles, a model is proposed to effectively support farmers in adopting AI technologies to boost agricultural performance. Originality/Value: The implementation of AI and ML tools in agriculture from diverse sources is done. This area needs study due to recent challenges faced by small and medium-scale farmers in the implementation of AI and ML tools in agriculture. The information acquired will help to create a new model by improving the outcomes of the existing scenario. Paper Type: Literature Review.