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This article contributes to knowledge on EU policy for Internet intermediaries by providing a characterization and analysis of the system of governance for intermediaries set out initially in the 2000 Directive on E-Commerce and recently updated in the 2022 Digital Services Act. The article shows how the new regulatory system of the DSA, unlike its predecessor, is underpinned by a strong European public transnational network governance approach, with a very noteworthy instantiation of regulatory responsibility at the EU level in respect of the power given to the European Commission to regulate Very Large Online Platforms (VLOPs) and Very Large Online Search Engines (VLOSEs). This reflects an attempt to mitigate the negative consequences of a largely light touch, self-regulated environment faced by Internet intermediaries. The article contends that the EU's new system of platform regulation creates instead a trans-European network (public regulatory dominated and epistemic regulatory actor enabled) more akin to the neoliberal model of EU telecommunications governance than the private interest self-regulatory aspirations of Internet governance specialists of the early 2000s, when the DEC was established. [ABSTRACT FROM AUTHOR]

Algorithms are rapidly transforming government bureaucracies. The implications of this transformation for the work of public service employees are not yet well understood. So far, the literature has mostly neglected the use of algorithms by these "screen-level bureaucrats", and this constitutes a major gap in our knowledge about how algorithms affect bureaucracies. To understand the work of screen-level bureaucrats and to explore how they actively engage with algorithmic software to support their assessment of online fraud, we analyzed 45 hours of observations and 12 in-depth interviews at the Netherlands Police. We employ a socio-material perspective to analyze the dynamics between screen-level bureaucrats and algorithms. We conclude that for administrative tasks, algorithms help screen-level bureaucrats to perform their work by providing structured data and allowing them to focus more on assessments which need a nuanced judgement. At the same time, algorithmic advice in a decision-making task is simply ignored by the screen-level bureaucrats as they predominantly rely on their professional judgement in the assessment of online fraud reports. This highlights the need to further investigate how an algorithm should not only provide accurate advice to the screen-level bureaucrats but also convince them to follow it. [ABSTRACT FROM AUTHOR]

With the increasing sophistication of online survey tools and the necessity of distanced research during the COVID-19 pandemic, the use of online questionnaires for research purposes has proliferated. Still, many researchers undertake online survey research without knowledge of the prevalence and likelihood of experiencing survey questionnaire fraud nor familiarity with measures used to identify fraud once it has occurred. This research note is based on the experience of researchers across four sites who implemented an online survey of families' experiences with COVID-19 in the U.S. that was subject to substantial fraud. By the end of data collection, over 70% of responses were flagged as fraudulent with duplicate IP addresses and concurrent start/end times representing the most common indicators of fraud observed. We offer lessons learned to illustrate the sophisticated nature of fraud in online research and the importance of multi-pronged strategies to detect and limit online survey questionnaire fraud. [ABSTRACT FROM AUTHOR]

The most popular payment method in recent years is the credit card. Due to the E-commerce industry’s explosive growth, the usage of credit cards for online purchases have been greatly increased as a result frauds has increased. Banks have been facing challenges to detect the credit card system fraud in recent years. Credit card fraud happens when the card was stolen for any unauthorized purposes or if the fraudster utilizes the credit card information for his own use. In order to prevent credit card fraud, it is essential to build detection measures. While detecting credit card theft with machine learning (ML), the features of credit card frauds play an important and they must be carefully selected. A fraud detection algorithm must be created in order to correctly locate and stop fraudulent activity as technology advances along with the amount of fraud cases. ML methods are essential for identifying fraudulent transactions. The implementation of fraud detection models is particularly difficult because of the sensitive nature of the data, the unbalanced class distributions, and the lack of data. In this work, an intelligent approach to detect and predict online fraud transaction using extreme gradient boosting (XGBoost) algorithm is described. The XGBoost model predicts whether a transaction is fraud or not. This model will achieve better performance interarm of recall, precision, accuracy and F1-score for credit card fraud detection. [ABSTRACT FROM AUTHOR]

In the digital era, online advertising fraud unfortunately undermines advertising and pricing cooperation in supply chains. In order to improve advertising effectiveness in a supply chain with a manufacturer and a retailer, this paper proposes three coordination mechanisms: cooperative advertising quality improvement (CQ), quality discount (QD) and a combination of the two (CQD). We derive the equilibrium advertising and pricing decisions in closed form under each mechanism. We show that improving advertising quality is always profitable for the two members, and that with QD included (either QD or CQD) can better coordinate the supply chain than CQ alone. However, the integration of a CQ and QD scheme is not always effective due to their interaction effects. Each mechanism can be achieved as an equilibrium strategy for supply chain members depending on their advertising effects. These insights offer theoretical evidence for the design of a supply chain coordination strategy. [ABSTRACT FROM AUTHOR]

Automated fraud detection can assist organisations to safeguard user accounts, a task that is very challenging due to the great sparsity of known fraud transactions. Many approaches in the literature focus on credit card fraud and ignore the growing field of online banking. However, there is a lack of publicly available data for both. The lack of publicly available data hinders the progress of the field and limits the investigation of potential solutions. With this work, we: (a) introduce FraudNLP, the first anonymised, publicly available dataset for online fraud detection, (b) benchmark machine and deep learning methods with multiple evaluation measures, (c) argue that online actions do follow rules similar to natural language and hence can be approached successfully by natural language processing methods. [ABSTRACT FROM AUTHOR]

Following the Ukraine humanitarian crisis, which was a result of the war in Ukraine, ordinary citizens stepped up to spontaneously coordinate various humanitarian responses to support affected populations. In this article such individuals who founded or coordinated this form of spontaneous humanitarian aid are referred to as citizen aid actors (CAA). This paper explores the work of citizen aid actors in Poland, a country which received many of the people displaced by the war in Ukraine. The study applies the concept of digital affordances to illustrate and argue that citizen aid actors relied on the features available on digital platforms to attain their goals. It is an outcome of a qualitative research approach where interviews, participant observation, and analysis of relevant online material about the studied citizen aid actors were used as methods of collecting data. The findings indicate that these actors used diverse digital platforms at different stages of the humanitarian aid projects, from establishment, organization and sustenance. While this study demonstrates how specific digital platforms contributed to the accomplishment of the work done by CAAs, it also highlights how problematic it can be when the actors largely rely on digital platforms. As such, potential risks associated with overreliance on digital platforms as a way of implementing projects are factored in. Ensuing concerns include the presence of online trolls, the spreading of fake news, and internet disruptions as potential obstacles for the success of the aid projects. [ABSTRACT FROM AUTHOR]

Industry 4.0 was born in 2011, for Indonesia it has brought benefits to industrialization and trade starting from 24-hour production automation, time efficiency because the process is fast and connected to the internet for unlimited distances with the help of internet signals, saving minds because AI-based technology is able to think to answer our questions and the ability to store and provide big data, production and trading costs are clearly more efficient because paperless, sending data without couriers, calculating the use of production inputs is more accurate so it is more economical. However, besides the advantages, there are also many challenging problems in Indonesia, starting from misuse of online media platforms, cyber crime (illegal taking of company data/hacking, misuse of other people's personal data for online fraud and illegal investment) which causes state losses of more than Rp. 232, 646 trillion until 2023. Therefore, the government has issued a policy breakthrough in the form of policies that have a broad impact and legal regulations that provide strict limits and sanctions [ABSTRACT FROM AUTHOR]

The purpose of this paper is to show fraud under the guise of online loans that harm the community and provide solutions so that people avoid these scams. In the Online Loan program, many people have complained about the dissemination of personal data to the authorities that have been carried out by the Online Loan organizer without prior notification from the party concerned. The use of data used in this paper comes from journal articles and also online news related to fraud cases under the guise of online loans that occur in Indonesia. The main factor in the rise of illegal online loans (Pinjol) is the unfulfilled needs and strong belief in money. The Financial Services Authority (OJK) said that the level of financial literacy and inclusion in the peer-to-peer financial technology (fintech P2P) technology industry or online loans (pinjol) is still relatively low. The results stated that it is necessary to implement stricter regulations, increase transparency, build a centralized database to track fraudulent activities, strengthen consumer protection laws, and provide legal solutions for fraud victims. With a comprehensive and coordinated approach, it is expected to reduce fraud cases in online loan services and increase public trust in this industry. [ABSTRACT FROM AUTHOR]

Phishing, an Internet fraud where individuals are deceived into revealing critical personal and account information, poses a significant risk to both consumers and web-based institutions. Data indicates a persistent rise in phishing attacks. Moreover, these fraudulent schemes are progressively becoming more intricate, thereby rendering them more challenging to identify. Hence, it is imperative to utilize sophisticated algorithms to address this issue. Machine learning is a highly effective approach for identifying and uncovering these harmful behaviors. Machine learning (ML) approaches can identify common characteristics in most phishing assaults. In this paper, we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing datasets. After that, we used the normalization technique on the dataset to transform the range of all the features into the same range. The findings of this paper for all algorithms are as follows in the first dataset based on accuracy, precision, recall, and F1-score, respectively: Decision Tree (DT) (0.964, 0.961, 0.976, 0.968), Random Forest (RF) (0.970, 0.964, 0.984, 0.974), Gradient Boosting (GB) (0.960, 0.959, 0.971, 0.965), XGBoost (XGB) (0.973, 0.976, 0.976, 0.976), AdaBoost (0.934, 0.934, 0.950, 0.942), Multi Layer Perceptron (MLP) (0.970, 0.971, 0.976, 0.974) and Voting (0.978, 0.975, 0.987, 0.981). So, the Voting classifier gave the best results. While in the second dataset, all the algorithms gave the same results in four evaluation metrics, which indicates that each of them can effectively accomplish the prediction process. Also, this approach outperformed the previous work in detecting phishing websites with high accuracy, a lower false negative rate, a shorter prediction time, and a lower false positive rate. [ABSTRACT FROM AUTHOR]

Fake profile detection on social media is a critical task intended for detecting and alleviating the existence of deceptive or fraudulent user profiles. These fake profiles, frequently generated with malicious intent, could engage in different forms of spreading disinformation, online fraud, or spamming. A range of techniques is employed to solve these problems such as natural language processing (NLP), machine learning (ML), and behavioural analysis, to examine engagement patterns, user-generated content, and profile characteristics. This paper proposes an automated fake profile detection using the coyote optimization algorithm with deep learning (FPD-COADL) method on social media. This multifaceted approach scrutinizes user-generated content, engagement patterns, and profile attributes to differentiate genuine user accounts from deceptive ones, ultimately reinforcing the authenticity and trustworthiness of social networking platforms. The presented FPD-COADL method uses robust data pre-processing methods to enhance the uniformness and quality of data. Besides, the FPD-COADL method applies deep belief network (DBN) for the recognition and classification of fake accounts. Extensive experiments and evaluations on own collected social media datasets underscore the effectiveness of the approach, showcasing its potential to identify fake profiles with high scalability and precision. [ABSTRACT FROM AUTHOR]

Mastering Mathematics can take a lot of effort but takes the proper skills and ongoing practice. Imparting knowledge to the students in mathematics subjects that are known to be difficult has been a challenge for math teachers. From modular to full face-to-face classes, there have been several adjustments and difficulties for teachers, which pushed the researchers, to come up with the study about the pedagogical modifications done by the teachers. In particular, the teachers' coping strategies and the milestones that they have achieved after classes will also be determined. Mathematics teachers at Badian National High School were chosen as ten participants in this qualitative study. Specifically, it utilized Heideggerian phenomenology and interpretative phenomenological analysis (IPA), anchored on the modified Van Kaam approach popularized by Moustakas. Hence, the following themes emerge: (1) The Adjustment, (2) The Struggle, (3) The Motivation, and (4) The Scheme. In conclusion, difficulties in online classes are the unstable internet connection and the need to focus on online discussions, while the adjustments in full face-toface classes are time management, financial problems, and the distance between the house and school. It is recommended to hear out the voices of the teachers in their difficult times, help address them, and might as well be given some benefits for all their efforts in teaching young minds. [ABSTRACT FROM AUTHOR]

Recently, graph neural networks (GNNs) have been widely used for e-commerce review fraud detection by aggregating the neighborhood information of nodes in various relationships to highlight the suspiciousness of nodes. However, existing GNN-based detection methods are susceptible to sample class imbalance and fraud camouflage problems, resulting in poor quality of constructed graph structures and inability to learn reliable node embeddings. To address the above problems, we propose a novel e-commerce review fraud detection method based on self-paced graph contrast learning (SPCL-GNN). Firstly, the method constructs a subgraph by initially selecting nodes through a labeled balanced extractor. Secondly, the subgraph connections are filtered and complemented by combining self-paced graph contrast learning and an adaptive neighbor sampler to obtain an optimized graph structure. Again, an attention mechanism is introduced in intra- and inter-relationship aggregation to focus on the importance of aggregation under different relationships. Finally, the quality of the node embedding representation is further improved by maximizing the mutual information between the local and global representations. Experimental results on the Amazon and YelpChi datasets show that SPCL-GNN significantly outperforms the baseline. [ABSTRACT FROM AUTHOR]

The rise of online‐to‐offline (O2O) e‐commerce business has brought tremendous opportunities to the logistics industry. In the online‐to‐offline logistics business, it is essential to detect anomaly merchants with fraudulent shipping behaviours, such as sending other merchants' packages for profit with their low discounts. This can help reduce the financial losses of platforms and ensure a healthy environment. Existing anomaly detection studies have mainly focused on online fraud behaviour detection, such as fraudulent purchase and comment behaviours in e‐commerce. However, these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package‐sending behaviours and the interpretable requirements of offline deployment in logistics. MultiDet, a semi‐supervised multi‐view fusion‐based Anomaly Detection framework in online‐to‐offline logistics is proposed, which consists of a basic version SemiDet and an attention‐enhanced multi‐view fusion model. In SemiDet, pair‐wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly instances. Then, SemiDet calculates the anomaly scoring of each merchant with an auto‐encoder framework. Considering the multi‐relationships among logistics merchants, a multi‐view attention fusion‐based anomaly detection network is further designed to capture merchants' mutual influences and improve the anomaly merchant detection performance. A post‐hoc perturbation‐based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end‐to‐end anomaly detection. The framework based on an eight‐month real‐world dataset collected from one of the largest logistics platforms in China is evaluated, involving 6128 merchants and 16 million historical order consignor records in Beijing. Experimental results show that the proposed model outperforms other baselines in both AUC‐ROC and AUC‐PR metrics. [ABSTRACT FROM AUTHOR]

Recently, fraud debt has been one of the major issues for Internet financial institutions. Due to fraudulent activities, huge losses are occurring in financial institutions. Hence, there is a need for a method of analyzing and detecting fraudulent transactions and separating them from genuine ones. Supervised learning approaches are mainly used for fraud detection since they consider the fraudulent set, which can be known from past transaction analysis. Though these models are interpretable, the prediction accuracy of these models remains challenging. However, these approaches fail to perform well when there are changes in customer behaviour. Moreover, it is complex to identify abnormal transactions due to data imbalance. Hence, this work presents a semi-supervised outlier score-based Anti-Fraud model to identify the loan applicant as a genuine or fraudulent debtor. The proposed work has the stages like a pre-processing module, Data augmentation and classification model. After per-processing the data, different outlier models such as Z-score and Isolation forest (IF) are applied to generate more data. Then, the Unsupervised K-Means Clustering (KMC) granularity-based Outlier scoring method is proposed to augment the datasets with too many scores. This clustering module clusters the loan applicants based on their credit history. Then, the Z-score and IF are applied to each cluster to augment the original dataset with different scores. This normalized data is input to the XGBoost-bidirectional Gated Recurrent unit (BiGRU) self-attention network (SAN). This XGB-BiGRU-SAN is used to capture more efficient dynamic information. Further, a mathematical model, an Arithmetic Optimization algorithm (AOA), is used to optimize the network weights. The performance of a proposed XGB-BiGRU-SAN Internet loan fraud detection is analyzed on the two benchmark datasets, like the leading club and bank loan status. The proposed XGB-BiGRU-SAN achieved better classification accuracy, precision and recall of 99.05%, 99.11% and 99.34% on the leading club dataset. Further, the accuracy, precision and recall values achieved in the bank loan status dataset are 98.67%, 98.82% and 98.62%, respectively. [ABSTRACT FROM AUTHOR]

In the present study we draw from data collected from 87 online romance fraudsters to explore whether and how offenders may suggest neutralizations to victims as a means to facilitate their crimes. Through thematic analysis of a series of e-mails exchanged with each fraudster we find that they encourage victims to neutralize their misgivings about sending them money in four overlapping ways. First, fraudsters appeal to vicarious necessity or implore victims to consider the fraudsters' needs. Second, they also appeal to an intimate relationship or suggest victims view their relationship as intimate in nature. Third, they deny susceptibility or subtly persuade victims to see themselves as holding more power in the interaction. Finally, fraudsters appeal to religious identity or encourage victims to consider their own religious identities. Our study contributes to understanding of neutralizations by explicating how neutralizations can be used by offenders as tools to assist their offending. It also adds to knowledge of the relationship between neutralizations and altercasting. In addition, we expand prior work illustrating the grooming process of online romance fraud and other crimes. We discuss implications for victim-blaming as it occurs among online romance fraud victims. [ABSTRACT FROM AUTHOR]

Online romance fraud is a form of cybercrime that has pervasive consequence on both individuals and organizations. As against previous studies that predominantly focus on male scammers, this study examines the influence of sociocultural experiences and technological modalities on women's involvement in online romance fraud in Tamale, Ghana. The study employs a blend of feminist criminology and Cressey's Fraud Triangle Theory to gain a more comprehensive understanding of the engagement of women in the scamming phenomenon in this region. Employing the experiences of fifteen (15) female scammers, the results show that sociocultural backgrounds of female scammers, combined with the internet's inherent opportunities, play a pivotal role in determining the engagement. The sociocultural background of women, coupled with the technological nature of the act, plays a significant role in driving their engagement in these illicit activities: motivation to offend and opportunity through social relationships. This study emphasizes how technological pathways enhance the opportunities available to women, highlighting the ways in which the digital landscape of such frauds potentially allows women to evade legal and societal repercussions. In conclusion, this study highlights the need for a comprehensive approach that considers both the technological aspects and the underlying societal constructs that facilitate them. [ABSTRACT FROM AUTHOR]

Media scholars have long recognized the potential for falsely balanced reporting to distort public opinion, but existing empirical evidence is inconclusive. In this study, we examine the effect of falsely balanced reporting and explicit journalistic intervention on perceptions of voter fraud in U.S. elections through original internet survey experiments conducted in the United States shortly before and after the 2020 U.S. presidential election held on November 3, 2020. The results show that exposure to falsely balanced reporting largely has a null effect on perceptions of voter fraud, though we also find evidence of partisan-based heterogeneity in its effect. The results of the study also show that explicit journalistic intervention equally decreases belief in voter fraud among both Democrats and Republicans before the election, but among Republicans the corrective effect of intervention disappears in the post-election period, suggesting that there are sharp contextual limits on the effect of explicit journalistic intervention. [ABSTRACT FROM AUTHOR]

The significant increase in credit card transactions can be attributed to the rapid growth of online shopping and digital payments, particularly during the COVID-19 pandemic. To safeguard cardholders, e-commerce companies, and financial institutions, the implementation of an effective and real-time fraud detection method using modern artificial intelligence techniques is imperative. However, the development of machine-learning-based approaches for fraud detection faces challenges such as inadequate transaction representation, noise labels, and data imbalance. Additionally, practical considerations like dynamic thresholds, concept drift, and verification latency need to be appropriately addressed. In this study, we designed a fraud detection method that accurately extracts a series of spatial and temporal representative features to precisely describe credit card transactions. Furthermore, several auxiliary self-supervised objectives were developed to model cardholders' behavior sequences. By employing intelligent sampling strategies, potential noise labels were eliminated, thereby reducing the level of data imbalance. The developed method encompasses various innovative functions that cater to practical usage requirements. We applied this method to two real-world datasets, and the results indicated a higher F1 score compared to the most commonly used online fraud detection methods. [ABSTRACT FROM AUTHOR]

Examination of the personal characteristics (i.e., gender, participation styles) of participants and the technical infrastructure (i.e., device type, internet connection type) of Instructional Discussion Forums (IDFs) can provide functional clues for mitigating social anxiety in these settings. In this context, this research investigated variances in learners' participation styles and social anxiety in IDFs in terms of gender and ICT availability. In addition, it explored the predictive relationships between participation styles and social anxiety. A sample of 272 freshman undergraduate students discussed various topics related to Internet ethics for three weeks through an IDF. The results indicated that participation styles differed by gender and ICT availability in several respects. First, handheld devices and cellular internet connections exerted a mediumsize effect on social anxiety. Further, "to get information" and "to fulfill requirement" purposes in the "Why" dimension of participation styles had a small predictive relationship with social anxiety, as did "analytical" and "practical" participation approaches in the "How" dimension. Of these, only the "to get information" purpose negatively predicted social anxiety. Overall, participation styles accounted for 19.1% of the variance in social anxiety. Based on the findings, future directions and practical implications are suggested for both researchers and practitioners. [ABSTRACT FROM AUTHOR]

Since the past, innovations have dramatically changed people's quality of life and played an important role in all aspects of humans' lives. The internet, social media, and e-books are well-known examples of current worldwide innovative breakthroughs. Despite innovations' advantages, they can lead to some negative consequences or harmful effects such as online fraud, copyright infringements and privacy violations which need to be regulated by law. In fact, the law seeks to regulate innovations in a way that protects users' rights while reducing risks. Every country have passed numerous lawsto regulate the online sphere and the European Union is a legislative body which has created various laws on different topics in its European member states. “The European Union” was established after the Second World War in the European region according to “the treaty of Maastricht”. Human dignity, freedom, equality, human rights, and the rule of law are the values of the European Union (The EU). Generally, the EU has four main institutions with important responsibilities. “The European commission” offers new laws and “The Parliament” and “The European Council” enact them in forms of directives and regulations. Additionally, the Commission monitors member states to ensure that rules are implemented correctly. The Court of Justice of the European Union (CJEU) interprets EU laws to ensure that they are applied uniformly in all EU countries, and resolves legal disputes between national governments and EU institutions. Therefore, EU has been active in making laws and implementing them. Therefore, since the emergence of new technologies, in particular the Internet, the EU has been constantly trying to solve the internet legal problems. In 2017, the Commission stated that digitization provides many benefits in terms of innovation, employment, growth and cultural diversity. Taking advantage of these opportunities requires a collective response to some of the challenges caused by digital transformation and a review of policies affected by digitalization. The EU has enacted many laws such as General Data Protection Regulation (GDPR) for protecting natural person’s data, the Digital Markets Act (DMA) with the aim of reaching potential economic growth in a coherent manner and the Digital Services Act (DSA) for safe and secure activities in the digital space. Now, the complicated innovation “Artificial Intelligence” (AI) has brought new legal problems. AI is not a new innovation. Actually, AI has been rooted in the previous century, when Alan Turing, a British mathematician, made The Bomb to encode the Enigma code which was used by the German army in the Second World War. After its successful function, working on AI has continued to grow with all of its ups and downs. Now, AI tries to simulate the function of human’s brain and on the ground of using new technologies, improved scientific methods and developed computer science, AI has improved and become generative. Generally, Generative AI works based on a complicated process which consists of deep learning, machine learning, natural language processing (NLP) and artificial neurons. These parts work completely related together to analyze and organize input data to answer users’ requests clearly and exactly. So, this function is like the human's brain function and has its difficulties, ambiguity and complexity. Also, artificial intelligence functions are broad and complex. This is used for a variety of purposes, from recipe suggestions to complex math problems and if it has mistaken, it can learn from them to correct its mistakes and improve its abilities. This technology helps human beings a lot in various majors such as medical diagnosis, automatic translations and natural disaster predictions. However, due to its game-changing nature, it poses new challenges for law enforcement and policymakers. Specifically, the European Union has tried to regulate Artificial intelligence with current laws and new policies. In 2018, In addition to emphasizing the guarantee of data protection and ethical standards, with the aim of preventing the risks of artificial intelligence and using its benefits, artificial intelligence was aligned with the legislative priorities of the European Union. In 2020, European Union published a report on "the European approach to excellence and trust" in artificial intelligence, which emphasized the existence of clear rules and a safe environment to build people's trust in artificial intelligence, with supporting the artificial intelligence growth and development. In order to synchronize laws related to artificial intelligence, a single and comprehensive law on artificial intelligence was reviewed by the European Commission, the Council of the European Union and the European Parliament. The draft was proposed by the European Commission in 2021 and approved by the European legislators in 2023. Despite the definitions and purposes in this draft, the risks of using AI systems are introduced and regulated. The use of AI systems to create or expand facial recognition databases, recognize people's emotions in workplaces and educational institutions, and using “biometric classification systems' are all examples of the inhibited risks of AI which are fortunately prohibited unless the law specifically allows for these risks. Additionally, there are high AI risks associated with AI systems in products. These products are protected by General Product Safety Directive (GPSD) 2001/95/EC and the new AI regulations. In addition, intelligence systems used in administrative, employment, immigration, asylum, and border management must be recorded in the EU data repositories. However, all challenges are not limited to risks. About copyright, directives and regulations have been in force since 1991 and now Directive 2019/790 contains provisions on the protection of copyright in the digital field. Therefore, when generative AI uses a lot of data for text and data mining (TDM) or other purposes, this data collection input must be used with their authors’ authorization. Additionally, after the content is produced, it must be shown that the content was created by artificial intelligence so that users can know whether the creator is human or not. In conclusion, this article endeavors to explain the history of the European Union and its groundbreaking legislative measures in different subjects, specifically in artificial intelligence. Some provisions of the draft of the AI Act are analyzed which are new in the world and they can affect other countries’ legislations in the AI field. [ABSTRACT FROM AUTHOR]

This document is an editorial published in the journal Frontiers in Psychology. The editorial discusses age differences in trust and fraud. It highlights the conflicting research on whether older adults are more vulnerable to fraud victimization than younger adults. The editorial also mentions some potential factors that may contribute to fraud vulnerability in older adults, such as cognitive declines and personality and social factors. The document includes a collection of articles that explore various aspects of fraud susceptibility, including psychosocial determinants, neural and biological mechanisms, and demographic predictors. Overall, the literature on age differences in fraud victimization does not reach a clear consensus, but there are different neural mechanisms for fraud regulation in younger and older adults. [Extracted from the article]