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editorial

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["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [3]=> object(Author)#849 (6) { ["_data"]=> array(15) { ["id"]=> int(9663) ["email"]=> string(23) "torok.bernat@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7684) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(7) "Török" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "Bernát" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" 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Artificial intelligence (AI) is widely used in social sciences and continues to evolve. Deep learning (DL) has emerged as a powerful AI tool transforming social sciences with valuable insights across many areas. Employing DL for modelling social sciences’ big data has led to significant discoveries and transformations. This study aims to systematically review and evaluate DL methods in social sciences. Following PRISMA guideline, this study identifies fundamntal DL methods applied to social science applications. We evaluated DL models using reported metrics and calculated a normalized reliability score for uniform assessment. Employing relief feature selection, we identified influential parameters affecting DL techniques’ reliability. Findings suggest evaluation criteria significantly impact DL model effectiveness, while database and application type influence moderately. Identified limitations include inadequate reporting of evaluation criteria and model structure details hindering comprehensive assessment and informed policy development. In conclusion, this review underscores DL methods’ transformative role in social sciences, emphasising the importance of explainability and responsibility.

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Deep learning (DL) has emerged as a cutting-edge data-driven methodology, revolutionizing the field of social sciences by providing profound insights. The application of DL techniques to model social science big data has resulted in significant discoveries and a rapid transformation of traditional methodologies. In this study, we aim to systematically review and evaluate the performance of DL methods in the social sciences. To ensure a rigorous and efficient exploration of relevant databases, we adhere to the PRISMA guidelines. Publications were sourced from Scopus and Web of Science (WoS). The search syntax encompassed essential DL methods, such as convolutional neural network (CNN), Long short-term memory (LSTM), deep neural network (DNN), deep belief network (DBN), Recurrent neural networks (RNN), and deep reinforcement learning (DRL), specifically applied to the social sciences. We utilized a comprehensive search filter to focus on the DL section and its various applications in the social sciences. These applications were categorized into twelve domains, including social information, social network analysis, social development, social movements, social inequalities, social cooperation, social conflict, social technology, social health, social risk, social environment, and social media. To evaluate the performance of DL models, we analyzed the evaluation metrics reported in each study. A normalized reliability score was calculated to facilitate a uniform evaluation of models across different applications. Furthermore, we employed a relief feature selection technique to identify the most influential parameter affecting the reliability score of DL techniques in social science applications. Our findings suggest that evaluation criteria play a crucial role in determining the effectiveness of DL models, while the influence of the database and application type is moderate. However, certain limitations were identified within the studies reviewed. One prominent limitation is the lack of reporting evaluation criteria values during the evaluation phase, which hinders a comprehensive assessment of the employed models...

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This article presents a state-of-the-art review of machine learning (ML) methods and applications used in smart grids to predict and optimise energy management. The article discusses the challenges facing smart grids, and how ML can help address them, using a new taxonomy to categorise ML models by method and domain. It describes the different ML techniques used in smart grids as well as examining various smart grid use cases, including demand response, energy forecasting, fault detection, and grid optimisation, and explores how ML can improve these cases. The article proposes a new taxonomy for categorising ML models and evaluates their performance based on accuracy, interpretability, and computational efficiency. Finally, it discusses some of the limitations and challenges of using ML in smart grid applications and attempts to predict future trends. Overall, the article highlights how ML can enable efficient and reliable smart grid systems.

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This article presents a state of the art review on machine learning (ML) methods and applications used in smart grids to predict and optimize energy management. The article discusses the challenges faced by smart grids and how ML can help, using a new taxonomy to categorize ML models by method and domain. It explains different ML techniques used in smart grids. It examines various smart grid use cases, including demand response, energy forecasting, fault detection, and grid optimization, and how ML can improve these cases. The article proposes a new taxonomy to categorize ML models and evaluates their performance based on accuracy, interpretability, and computational efficiency. Finally, it discusses limitations, challenges and future trends of using ML in smart grid applications. Overall, the article highlights how ML can enable efficient and reliable smart grid systems.

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Annamária, Makó Csaba" } ["subtitle"]=> array(2) { ["en_US"]=> string(75) "A Systematic Review, Novel Taxonomy, and Comparative Performance Evaluation" ["hu_HU"]=> string(75) "A Systematic Review, Novel Taxonomy, and Comparative Performance Evaluation" } ["title"]=> array(2) { ["en_US"]=> string(31) "Machine Learning in Smart Grids" ["hu_HU"]=> string(31) "Machine Learning in Smart Grids" } ["locale"]=> string(5) "en_US" ["authors"]=> array(6) { [0]=> object(Author)#823 (6) { ["_data"]=> array(15) { ["id"]=> int(9667) ["email"]=> string(20) "dul.janos@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6999) ["seq"]=> int(2) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(7) "Rituraj" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "Rituraj" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [1]=> object(Author)#857 (6) { ["_data"]=> array(15) { ["id"]=> int(9668) ["email"]=> string(20) "dul.janos@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6999) ["seq"]=> int(2) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(12) "Várkonyi T." 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The dynamic evolution of artificial intelligence (AI) and machine learning (ML) tools poses challenges to the existing liability concepts. This paper aims to examine some of the fields of tortious liability that are most affected by these developments to analyse whether the existing legal standards in civil liability can still be used, with slight reinterpretation, when approaching liability scenarios related to AI and ML, and whether fine tuning of the existing liability regimes is needed, or novel liability scenarios should be established. To answer this question, the paper begins by examining the nature of the regulation of AI and ML: whether it should be a regulatory regime neutral to technology or whether, instead, a sector specific approach is essential. The study considers the already existing legal authorities of the EU and the U.S. as starting points for the analysis, and briefly examines the interpretations municipal courts apply when deciding in AI and ML related tort cases.

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This paper discusses the recently emerging platform law from a jurisprudential point of view. After defining the platform as a general coordination mechanism, it deals with topics such as the rationale for regulation, its main goals, and its general characteristics. According to the study, the main argument for regulation is that the platform, as a coordination mechanism, tends to become unstable without intervention, or to become harmful from the point of view of society. Above all, it tends to abuse the asymmetric power situation that exists between the platform and its users. These conditions must be prevented from occurring and platform users must be protected in certain situations. The study lists four features that characterise platform law: its ex ante regulatory nature, the predominance of technology regulation and self-regulation, and the extensive use of user protection tools, such as complaint mechanisms, protection of user accounts and explainability obligations. This toolbox partly resembles the long-established methods of consumer protection, but it also differs from it in certain ways.

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A cikk a közelmúltban kialakuló platformjogot jogelméleti szempontból tárgyalja. A platform általános koordinációs mechanizmusként való meghatározása után olyan témákkal foglalkozik, mint a szabályozás logikája,  fő céljai, általános jellemzői. A szabályozás fő indokaként a tanulmány azt állítja, hogy a platform, mint koordinációs mechanizmus beavatkozás nélkül hajlamos instabillá válni, illetve a társadalom szempontjából káros állapotba kerülni. Mindenekelőtt hajlamos visszaélni a platform és felhasználói között fennálló aszimmetrikus hatalmi helyzettel. Ezeket az állapotokat meg kell akadályozni, és bizonyos helyzetekben meg kell védeni a felhasználókat. A tanulmány négy olyan jellemzőt sorol fel, amelyek a platformjogot jellemzik: az ex ante szabályozási jelleg, a technológiai szabályozás és önszabályozás túlsúlya, valamint a felhasználóvédelmi eszközök, így a panaszmechanizmusok, a felhasználói fiókok védelme és a megmagyarázhatósági kötelezettségek kiterjedt alkalmazása. Ez utóbbi eszköztár részben hasonlít a fogyasztóvédelem régóta ismert módszereire, de bizonyos ponton el is tér attól.

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Based on Statistics Finland’s Quality of Work Life Survey 2018, this paper seeks how Finnish employees’ use of digital tools differs from each other, what sociodemographic and work contextrelated factors these differences are connected to, and how differences in usage are reflected in the effects of digitalisation on employees’ work. The research identified five user groups. Nearly half of the employees are classified as Skilled Users, which are typically of a young age. Challenges for other groups include deficiencies in digital skills, problems in learning to use digital tools, routine-like usage, low learning demands at work, and a high workload and learning pressure arising from intensive use of digital tools. The results support the sequential and compound digital exclusion arguments derived from previous literature, but do not fully support the stratification argument. The paper shows that among employees there are digital divides of various types. Narrowing these gaps requires different policies and customised solutions.

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This paper focuses on the increasing prominence of digital labour platforms in the labour markets of Southeast Europe, and compares the supply of online labour from nine selected countries: Serbia, Romania, Hungary, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, and Bulgaria. Digital labour platforms, as an innovative business model, play an important role in today’s labour markets by linking the demand and supply of digital work. Southeast Europe is no exception to this trend, and has become an important supplier of online labour. With the impact of the Covid–19 pandemic, this and other new forms of employment further increased both globally and in Southeast Europe. Despite this trend, online labour often remains invisible and under the radar of national policymakers and regulators, as well as national statistical agencies, due to the globalised nature of online platforms. This paper aims to shed light on the development of online labour in the countries studied, based on publicly available data collected through Gigmetar, a web scraping tool designed to monitor trends on the number, gender, incomes, and occupations of online workers. The paper compares online labour from nine countries active on the most significant general digital labour platforms (Upwork, Freelancer, and Guru) from February 2022 to October 2022. The criteria for the comparison include occupations, gender and income. The analysis is based on the data of approximately 80% of the total number of active digital workers on the platforms under investigation.
The paper points out the similarities and differences in online labour between the countries of Southeast Europe. For example, the number of online workers increased in all the countries, with creative services and multimedia and software development comprising the most dominant occupations in each country. Moreover, men are more commonly represented in these digital markets than women.

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This paper focuses on the increasing prominence of digital labour platforms in the labour markets of Southeast Europe and compares the supply of online labour from 9 selected countries: Serbia, Romania, Hungary, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, and Bulgaria. Digital labour platforms, as an innovative business model, play an important role in today’s labor markets by linking the demand and supply of digital work. This is no exception in Southeast Europe which has become an important supplier of the online labour. With the COVID-19 pandemic, this and other new forms of employment further increased both globally and in Southeast Europe. Despite this trend, online labour often remains invisible and under the radar of national policy makers and regulators, as well as national statistical agencies due to the global nature of the online platforms.

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Digital technologies can considerably increase the visibility of people’s behaviours and activities, and therefore researchers should pay more attention to visibility and opaqueness in organisations. This paper focuses on visibility in terms of the information given or mediated to workers. The aim of this paper is to examine consequences of visibility for workers who carry out work tasks through digital labour platforms. The research will focus on how visibility or opaqueness in practice promotes or hinders workers’ capacity to act and to make informed choices in their work. The visibility paradoxes of connectivity, performance and transparency are used as methodical lenses.
The same platform operations can have both empowering and marginalising consequences for workers. While labour platforms continuously improve visibility to workers, they may also hide, inadvertently or intentionally, key information.

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Leonardi & Treem (2020:1602) argue that while digitization, digitalization and datafication afford a massive increase in the behavioral visibility of actors, academic research needs to better examine, how transparency and visibility are performed, managed and evaluated in organizations, not forgetting the important role of connectivity in these processes. Visibility can create digital trust between strangers. It is of interest, how algorithmic systems mediating visibility become embedded in the networks of people and existing systems that make use of them, and with what consequences. For understanding the processes of visibility, qualitative and ethnographic research is needed.

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This research examines the work organisation of the Foodpanda food delivery firm and the experiences of the bicycle couriers who work for it, particularly their attitudes to the algorithmic management of their work. The focus of the inquiry is the gamification of work, both from-above and from below. Gamification from-above is constructed by the management. Taking part in the games can be a source of pride and satisfaction, but also of addiction and self-exploitation. Gamification from-below includes all kinds of “games” that the couriers initiate. These can be different strategies to earn more money, save energy or sabotage the labour process. The study shows the connection between games and the formation of consent and resistance among the couriers. The analysis differentiates between the games of making do and making out. Games of making do usually bring about consent, as they stay within the boundaries set by the management. In contrast, making out goes against managerial interest and gives agency to the couriers, thus it has the potential to foster resistance.

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The research examines the work organization of Foodpanda and the bicycle couriers’ experiences and attitudes regarding the algorithmic management of their work. The focus of the inquiry is the gamification of the work, from-above and from-below. In the first case, gamification is created from the side of the management, while in the second, games are initiated by workers.

Gamification from above consists of the gambling-like work process, the ranking of the couriers and the bonuses offered for completing “challenges” during work. The research found that taking part in the games can cause addiction and self-exploitation among couriers. Furthermore, successful participation in the game leads to pride and recognition from other workers. Gamification from below includes all kinds of “games” that the couriers initiate. These can be different strategies to earn more, while sparing energy; small sabotages of the application and bets among one another.

The study shows the connection between games and the formation of consent among the couriers. The findings conclude that by taking part in the games from-above, the couriers must accept the rules and the logic of the work organization. Furthermore, the games give space for relative satisfaction with one’s work. Therefore, the games from-above contribute to the formation of consent to the algorithmic work management. On the other hand, some games from-below give agency to the couriers, thus have the potential to advocate resistance. Nevertheless, the research found that the majority of games from-below (as for now) do not cause harm to the interest of the company.

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Cycling food couriers in Hungary tend to normalise and justify for themselves the precarious gig working conditions as a sports activity. To understand the blurring between sport and work, I carried out participant observation, conducted semi-structured interviews and discourse analysis. I worked as a bicycle courier in Budapest in July and August 2021. The successful boom of the cycling-based food delivery platforms depends on the extraction of bodily resources. Food delivery companies create new frontiers as they frame labour as challenging cardio activity.
The riders embrace the idea that they get paid for training their body, which activity is otherwise expensive and tiring. The workers utilise their knowledge from their past sporting activities about nutrition and pain relief to increase their workload. Sporting rivalry and boasting of results are active features of the courier community.
Although my interviewees proudly claimed themselves entrepreneurs, the body experiences reveal the cleavage between gig wage labour and idealised entrepreneurship. The pain and dangers of urban cycling work highlight the unequal relationship and make couriers critical of the company.

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Az ételkiszállítás a platformgazdaság egyik leglátványosabb ágazata, amely az elmúlt években több ezer embert szerződtetett Budapesten. Ez a kutatás azt vizsgálja, hogy a kerékpáros ételkiszállítók hogyan fogadják el, normalizálják és igazolják a bizonytalan munkakörülményeket, a munkaerő kizsákmányolását és a kockázatot. Az ételkiszállítási ágazat kritizálásának és mégis elfogadásának paradoxona aktív jellemzője a magyarországi futárközösségnek. A futárok vezető Facebook-csoportja és az általam készített előzetes interjúk tele vannak a futárcégekkel szembeni kemény kritikákkal. A futárok azonban továbbra is szerződésben állnak ezekkel a cégekkel, és büszkén vállalják a közös futáridentitást. Hogyan fogadják el, normalizálják és igazolják a magyarországi ételfutárok a bizonytalan munkakörülményeket, a munkaerő kizsákmányolását és a kockázatot?
A magyarországi kerékpáros ételfutárok hajlamosak sporttevékenységként normalizálni és igazolni maguk számára a bizonytalan munkakörülményeket. A sport és a munka közötti elmosódás megértése érdekében résztvevő megfigyelést végeztem, félig strukturált interjúkat készítettem és diskurzuselemzést végeztem. Kerékpáros futárként dolgoztam Budapesten 2021 júliusában és augusztusában.
A kerékpáros ételkézbesítő platformok sikeres fellendülése a testi erőforrások kitermelésétől függ. Az ételkiszállító cégek új határokat teremtenek, a munkát kihívást jelentő kardiótevékenységként keretezik. A kerékpárosok elfogadják az ideát, hogy fizetést kapnak a testük edzéséért, amely tevékenység egyébként költséges és fárasztó. A munkások a korábbi sporttevékenységekből származó táplálkozási és fájdalomcsillapítási ismereteiket használják fel a munkaterhelés növelésére. A sportversenyzés és az eredményekkel való dicsekvés aktív jellemzője a futóközösségnek.
Bár interjúalanyaim büszkén állították, hogy vállalkozók, testük tapasztalatai a platform munka és az idealizált vállalkozói lét közötti szakadékot mutatják. A városi kerékpáros munka fájdalmai és veszélyei rávilágítanak az egyenlőtlen viszonyra, és kritikussá teszik a futárokat a vállalkozással szemben.

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European Data Protection Supervisor (EDPS) Giovanni Buttarelli’s posthumous manifesto, Privacy 2030: A New Vision for Europe, places data protection in a global context. Competition and data protection authorities within the EU cooperate and share information about their official inquiries. If properly enforced, the GDPR may be an effective tool of transparent data processing in the EU, and can serve as a model for the rest of the world. Enforcement is the duty of Member States’ DPAs, therefore, it may be worth analysing Buttarelli’s views in relation to the issues currently facing Hungarian data protection regulation. The paper critically presents Buttarelli’s main views, while discussing them in relation to Hungarian public administration through a specific legal case. As a result of the comparative analysis, it can be concluded that by enhancing the data protection culture and its administrative enforcement, our personal data can be better protected.

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European Data Protection Supervisor (EDPS) Giovanni Buttarelli’s posthumous manifesto, „Privacy 2030: A New Vision for Europe”, places data protection in a global context. In his view, a digital underclass has emerged with members who have no access to the necessary informations to understand the logic of the algorithmic decisions affecting them and their privacy. Competition and data protection authorities within the EU cooperate and share their informations about their investigations. While data maximisation is clearly unsustainable from an environmental perspective, within the EU, data minimisation is a core principle of data protection law. Personal data should serve the public interest of state and society rather than private companies based mostly in the US and China.

In case of its proper enforcement, GDPR may be an effective tool of transparent data processing in the EU, and can serve as a model for the rest of the world. Enforcement is duty of the member states’ authorities. Therefore, Buttarelli’s views and Hungarian data protection’s legal tools are worth a comparative analysis.

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