Protect User Privacy Research Articles

Evolution of Libraries in the Digital Era: Redefining Access, Education, and Cultural Preservation

The digital revolution has significantly reshaped libraries, transforming them from traditional repositories into dynamic, technology-driven centres of information, education, and cultural preservation. This study investigates the comprehensive digital transformation of libraries, with a focus on the integration of electronic resources, virtual reality (VR), augmented reality (AR), digital catalogues, multimedia labs, and maker spaces. Utilizing a multi-method approach, the research analyzes statistical data on user engagement, revealing a consistent 20% annual increase in users accessing e-books and journals globally since 2010. Additionally, surveys conducted among students and researchers in Azerbaijan indicate a 25% rise in electronic resource usage since 2015, with modern search tools enhancing information retrieval speed by 30%. The incorporation of VR and AR technologies is explored for their potential to enrich educational experiences and preserve cultural heritage. At the same time, multimedia labs and maker spaces are examined for their roles in fostering innovation and skill development. Despite these advancements, libraries face significant challenges such as the digital divide, information overload, privacy concerns, data security, and ensuring accessibility for all users. Libraries have started to address these challenges through continuous adaptation, investing in cutting-edge technologies, and developing digital literacy programs to bridge the digital divide, manage information effectively, and protect user privacy. The findings highlight the critical role of libraries in the digital age, underscoring their evolution into inclusive, technologically advanced institutions that support education, research, and cultural enrichment. The study underscores the importance of sustained investment in digital infrastructures to maintain libraries' pivotal role in modern society.

Response From Crisis Text Line to "Commentary on 'Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line'".

Response From Crisis Text Line to "Commentary on 'Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line'".

Authors' Reply: Commentary on "Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line".

We appreciate Reierson's thoughtful commentary on our 2019 paper, which described our experiences, ethical process, judgment calls, and lessons from a 2016-2017 data-sharing pilot between Crisis Text Line and academic researchers. The commentary raises important questions about the ethical conduct of health research in the digital age, particularly regarding informed consent, potential conflicts of interest, and the protection of vulnerable populations. Our article focused specifically on the noncommercial use of Crisis Text Line data for research purposes, so we restrict our reply to points relevant to such usage. While we acknowledge the limitations of Crisis Text Line's Terms of Service as a means of informing users about data sharing for research, we maintain that our guidelines were ethically sound and aligned with well-established practices for institutional review board (IRB) review and researcher training. We emphasize the critical role of IRBs in ensuring that research involving vulnerable populations, including minors, is conducted ethically and with appropriate safeguards. Regarding potential conflicts of interest, we argue that unpaid, nonfiduciary advisory board service for a nonprofit organization does not constitute a conflict requiring disclosure. The transparent nature of our collaboration with Crisis Text Line, as evidenced by the authorship and acknowledgments in our paper, further underscores our commitment to ethical research practices. We recognize the complexity and evolving nature of the challenges surrounding data-sharing partnerships in digital health research. As the field progresses, we remain committed to ongoing, transparent engagement and to refining best practices in collaboration with colleagues, stakeholders, and the public. Our response aims to provide clarity and context for the concerns raised in the commentary while reaffirming the integrity and value of our original work. Ultimately, we maintain that our paper contributed meaningfully to the ongoing discourse on ethical data sharing and laid the groundwork for future improvements in this critical area of digital health research.

Advancing Privacy-Aware Machine Learning on Sensitive Data via Edge-Based Continual μ-Training for Personalized Large Models

Abstract This paper introduces an innovative method for fine-tuning a large multi-label model for abnormality detection, utilizing a smaller trainer and advanced knowledge distillation techniques. It studies the effects of fine-tuning on various abnormalities, noting different improvements based on the Original Model’s performance in specific tasks. The experimental setup, optimized for on-device inference and fine-tuning with limited computational resources, demonstrates moderate yet promising enhancements in model performance post-fine-tuning. Key insights from the study include the significance of aligning the μ-Trainer’s behavior with the Original Model and the influence of hyper-parameters like batch size on fine-tuning outcomes. The research acknowledges limitations such as the limited exploration of loss functions in multi-label models and constraints in architectural design, suggesting potential avenues for future investigation. While the proposed Naive Continual Fine-tuning Process is in its early stages, we highlight this paper’s potential model personalization on long-term data. Moreover, weight transfer in our system is exclusively for fine-tuning; hence, it improves user privacy protection by failing data reconstruction attempts from weights, like an issue with Federated Learning models. Our on-device fine-tuning prevents the transferring of data or gradients from the edge of the network to their server. Despite modest performance improvements after fine-tuning, these working layers represent a small fraction (0.7%) of the total weights in the Original Model and 1.6% in the μ-Trainer. This study establishes a foundational framework for advancing personalized model adaptation, on-device inference and fine-tuning while emphasizing the importance of safeguarding data privacy in model development.

Decentralized Federated Learning with Prototype Exchange

As AI applications become increasingly integrated into daily life, protecting user privacy while enabling collaborative model training has become a crucial challenge, especially in decentralized edge computing environments. Traditional federated learning (FL) approaches, which rely on centralized model aggregation, struggle in such settings due to bandwidth limitations, data heterogeneity, and varying device capabilities among edge nodes. To address these issues, we propose PearFL, a decentralized FL framework that enhances collaboration and model generalization by introducing prototype exchange mechanisms. PearFL allows each client to share lightweight prototype information with its neighbors, minimizing communication overhead and improving model consistency across distributed devices. Experimental evaluations on benchmark datasets, including MNIST, CIFAR-10, and CIFAR-100, demonstrate that PearFL achieves superior communication efficiency, convergence speed, and accuracy compared to conventional FL methods. These results confirm PearFL’s efficacy as a scalable solution for decentralized learning in heterogeneous and resource-constrained environments.

Perspective: Challenges for Personalized Nutrition in the Current U.S. Regulatory Framework and Future Opportunities.

Personalized Nutrition (PN) aims to provide tailored dietary recommendations to improve a person's health outcomes by integrating a multitude of individual-level information and support desired behavior changes. The field is rapidly evolving with technological advances. As new biomarkers are discovered, wearables and other devices can now provide up-to-the-minute insights, and artificial intelligence (AI) and machine learning (ML) models support recommendations and lifestyle behavior change. Advancements in the field enhance the potential for tailored and effective programs but raises important questions regarding user safety, security, health, transparency, and privacy. The Personalized Nutrition Initiative at the University of Illinois held the workshop, "Challenges for Personalized Nutrition in the Current U.S. Regulatory Framework and Future Opportunities," to address regulatory implications of current PN programs, future innovation within the current framework, and existing complexities of oversight. A majority of PN programs combine multiple components and these components may need to be regulated differently. The areas of food, supplements, in vitro diagnostics, and medical and wellness devices were described and discussed as they apply to PN programs. The speakers and discussants concluded that regulatory guidance for PN programs should focus on ensuring (i) safety and accuracy of the tests and devices, (ii) credentialed and skilled experts develop the advice, (iii) responsible and clear communication of information and benefits, (iv) substantiation of scientific claims, and (v) procedures are implemented to protect user privacy. Furthermore, as this field incorporates new devices, biomarkers, behavior-based tools, and the integration of AI and ML, the need to adapt the existing regulatory framework was also considered. Working closely with regulatory bodies is required and should be an opportunity to provide users with transparency, build trust, and create a source of differentiation for PN innovators.

Commentary on "Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line".

Commentary on "Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line".

WebTrackingScore: A Combined Web Tracking Risk Score System for Websites

Privacy awareness has increased in recent years. In many countries, websites warn users about their use of cookies and Web browsers provide mechanisms to protect user privacy. However, it is not easy for users to understand tracking risks associated with browsing a given website. To address this, several privacy scanners have emerged to evaluate the privacy practices of websites. However, the scores these scanners provide are often limited to a small number of Web tracking indicators and are thus incomplete. To overcome this limitation, in this paper, we present WebTrackingScore, which provides users with a simple and intuitive indication of the privacy risk associated with visiting a website from a tracking perspective. Its design is based on an in-depth analysis of tracking mechanisms and the behaviour of some of the most frequently visited sites. Based on WebTrackingScore, we have analysed how the most popular websites are classified.

Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy-Added Federated Learning Settings: Quantitative Study.

The rise of wearable sensors marks a significant development in the era of affective computing. Their popularity is continuously increasing, and they have the potential to improve our understanding of human stress. A fundamental aspect within this domain is the ability to recognize perceived stress through these unobtrusive devices. This study aims to enhance the performance of emotion recognition using multitask learning (MTL), a technique extensively explored across various machine learning tasks, including affective computing. By leveraging the shared information among related tasks, we seek to augment the accuracy of emotion recognition while confronting the privacy threats inherent in the physiological data captured by these sensors. To address the privacy concerns associated with the sensitive data collected by wearable sensors, we proposed a novel framework that integrates differential privacy and federated learning approaches with MTL. This framework was designed to efficiently identify mental stress while preserving private identity information. Through this approach, we aimed to enhance the performance of emotion recognition tasks while preserving user privacy. Comprehensive evaluations of our framework were conducted using 2 prominent public datasets. The results demonstrate a significant improvement in emotion recognition accuracy, achieving a rate of 90%. Furthermore, our approach effectively mitigates privacy risks, as evidenced by limiting reidentification accuracies to 47%. This study presents a promising approach to advancing emotion recognition capabilities while addressing privacy concerns in the context of empathetic sensors. By integrating MTL with differential privacy and federated learning, we have demonstrated the potential to achieve high levels of accuracy in emotion recognition while ensuring the protection of user privacy. This research contributes to the ongoing efforts to use affective computing in a privacy-aware and ethical manner.

Enhancing Autonomous Vehicle Safety with Blockchain Technology: Securing Vehicle Communication and AI Systems

In recent years, the rapid development of autonomous vehicles (AVs) has brought new challenges in terms of data security, privacy, and communication integrity. Our research investigates the potential of blockchain technology to improve the security of AVs by securing vehicle communication systems. By integrating blockchain with AI-based predictive algorithms, this approach aims to secure vehicle peer-to-peer communication, reduce traffic congestion, and improve safety for drivers and pedestrians. Blockchain’s decentralized ledger ensures the integrity of data exchange between vehicles and smart city infrastructure and mitigates the risks of cyberattacks such as data manipulation and identity forgery. This paper also examines recent advances in vehicular ad hoc networks (VANETs) and vehicular social networks (VSNs), and it demonstrates how the immutability and cryptographic security of the blockchain can strengthen AV systems. The proposed architecture not only protects user privacy but also decentralizes access to critical data needed for AI-driven decisions, ultimately promoting a safer and more reliable environment for autonomous vehicles.

User Data and Digital Privacy: Privacy Policies of Social Media Platforms

This study examines how user data are collected, processed, and protected by analyzing the privacy policies and personal privacy violations of five of the world’s most widely used social media platforms (Facebook, Instagram, X [Twitter], YouTube and TikTok). This research adopts a qualitative approach and analyses current user data and past privacy violations through document analysis and case studies. It also evaluates platforms’ compliance with regulations such as GDPR and KVKK. The study revealed that platforms provide transparency in data management; however, users have difficulty understanding complex privacy policies and therefore cannot provide informed consent. Incidents such as Facebook’s Cambridge Analytica scandal and TikTok’s data breach highlight the need for stronger data security measures and user-friendly control mechanisms. This study states that both users and platforms should take responsibility for protecting user privacy. Platforms should develop clearer privacy policies and strengthen data security, while users should increase their media literacy and manage their data effectively. In addition, regulators should ensure the enforcement of privacy laws and introduce stricter penalties for violations. The study concludes that more research is required on data privacy, cultural factors influencing user behavior, and the impact of new technologies, such as artificial intelligence, on privacy practices.

Advertising Breaks the Privacy of WeChat Circle of Friends Analysis

With the rapid development of Internet technology and the popularity of smart phones, social media has become an indispensable part of People's Daily life. WeChat, one of the largest social media platforms in China, has gained popularity for its moments function due to its high degree of privacy and user viscosity. However, with the rise of the social media advertising market, the frequent appearance of advertising messages in WeChat moments not only disrupts the social experience of users, but also may cause users to worry about privacy leakage. This study aims to deeply analyze how advertisement delivery breaks the privacy of WeChat circle of friends, and explore the possibility of optimizing advertisement delivery strategy, including delivery quantity, delivery characteristics, target audience positioning and so on. Through the optimization strategy, the purpose is to reduce the intrusion of user privacy, improve user experience, and achieve a win-win situation between business interests and user privacy protection. Using questionnaire survey and hot list comparison, the study analyzed the impact of advertising on users' privacy perception, attitude and behavior. Suppose the results show that AD delivery breaks the privacy of WeChat moments to some extent, and users are put off by frequent and non-personalized ads. The study also found that gender, age, occupation and other factors have an impact on the perception of privacy in WeChat circle of friends. Based on the research results, marketing strategies and content suggestions for different user groups are proposed, in order to better safeguard users' privacy rights and social experience while ensuring the effect of advertising delivery and promote the sustainable development of social media platforms and advertising industry.

Privacy-Preserving Sequential Recommendation with Collaborative Confusion

Sequential recommendation has attracted a lot of attention from both academia and industry, however the privacy risks associated with gathering and transferring users’ personal interaction data are often underestimated or ignored. Existing privacy-preserving studies are mainly applied to traditional collaborative filtering or matrix factorization rather than sequential recommendation. Moreover, these studies are mostly based on differential privacy or federated learning, which often lead to significant performance degradation, or have high requirements for communication. In this work, we address privacy-preserving from a different perspective. Unlike existing research, we capture collaborative signals of neighbor interaction sequences and directly inject indistinguishable items into the target sequence before the recommendation process begins, thereby increasing the perplexity of the target sequence. Even if the target interaction sequence is obtained by attackers, it is difficult to discern which ones are the actual user interaction records. To achieve this goal, we introduce a novel sequential recommender system called CLOUD ( C o L laborative-c O nfusion seq U ential recommen D er), which incorporates a collaborative confusion mechanism to modify the raw interaction sequences before conducting recommendation. Specifically, CLOUD first calculates the similarity between the target interaction sequence and other neighbor sequences to find similar sequences. Then, CLOUD considers the shared representation of the target sequence and similar sequences to determine the operation to be performed: keep, delete, or insert. A copy mechanism is designed to make items from similar sequences have a higher probability to be inserted into the target sequence. Finally, the modified sequence is used to train the recommender and predict the next item. We conduct extensive experiments on three benchmark datasets. The experimental results show that CLOUD achieves a maximum modification rate of 66.57% on interaction sequences, and obtains over 99% recommendation accuracy compared to the state-of-the-art sequential recommendation methods. This proves that CLOUD can effectively protect user privacy at minimal recommendation performance cost, which provides a new solution for privacy-preserving for sequential recommendation. Our implementation is available at https://github.com/weiwang0927/CLOUD .

Reexamination of the realtime protection for user privacy in practical quantum private query

Abstract Quantum private query (QPQ) is the quantum version for symmetrically private information retrieval. However, the user privacy in QPQ is generally guarded in the delayed and cheat-sensitive way. That is, the dishonest database holder Bob’s cheating to elicit user privacy can only be discovered after the protocol is finished (when the user finds some errors in the retrieved database item). Such delayed detection may cause very unpleasant results for the user Alice in real-life applications. Without the realtime conscious of privacy leaking, Alice may make faulty decisions according to the received faulty item and suffer great loss. Worse yet, it is difficult for Alice to accuse a dishonest database to any one else though she can detect the cheating. Current efforts to protect user privacy in realtime in existing QPQ protocols mainly use two techniques, i.e. adding an honesty checking on the database or allowing the user to reorder the qubits. We reexamine these two kinds of QPQ protocols and find neither of them can work very well. We give concrete cheating strategies for both participants and show that honesty checking of inner participant should be dealt more carefully in for example the choosing of checking qubits. Finally, we give an idea to realize realtime detection of dishonest database in QPQ of blocks (i.e. multi-bit database items).

Analysis of Software Development and Operation Measures from the Perspective of Security Technology

Security technology is crucial in software development and operation in the digital age. Secure software can protect user privacy and data security, prevent hacker attacks and data breaches, ensure legitimate business operations, and protect core assets. However, the development process often faces threats such as injection attacks, Cross-Site Scripting (XSS), and Cross-Site Request Forgery (CSRF), mainly due to code vulnerabilities, configuration errors, and risks from third-party components. To meet these challenges, this paper discusses the application of security technology in development and operation, emphasizing security requirements analysis, design principles, coding practices, and testing during the development phase. Along with focusing on environmental configuration, continuous monitoring, emergency response, disaster recovery, and regular auditing and updating during the operation phase. These measures can significantly enhance the security of software systems and protect user and corporate data.

ВЕЛИКІ МОВНІ МОДЕЛІ ТА ОСОБИСТА ІНФОРМАЦІЯ: ПРОБЛЕМИ БЕЗПЕКИ ТА ШЛЯХИ ЇХ ВИРІШЕННЯ ЗА ДОПОМОГОЮ АНОНІМІЗАЦІЇ

In light of the growing capabilities of Large Language Models (LLMs), there is an urgent need for effective methods to protect personal data in online texts. Existing anonymization methods often prove ineffective against complex LLM analysis algorithms, especially when processing sensitive information such as medical data. This research proposes an innovative approach to anonymization that combines k-anonymity and adversarial methods. Our approach aims to improve the efficiency and speed of anonymization while maintaining a high level of data protection. Experimental results on a dataset of 10,000 comments showed a 40% reduction in processing time (from 250 ms to 150 ms per comment) compared to traditional adversarial methods, a 5% improvement in medical data anonymization accuracy (from 90% to 95%), and a 7% improvement in data utility preservation (from 85% to 92%). Special attention is paid to the application of the method in the context of interaction with LLM-based chatbots and medical information processing. We conduct an experimental evaluation of our method, comparing it with existing industrial anonymizers on real and synthetic datasets. The results demonstrate significant improvements in both data utility preservation and privacy protection. Our method also takes into account GDPR requirements, setting a new standard in the field of data anonymization for AI interactions. This research offers a practical solution for protecting user privacy in the era of LLMs, especially in sensitive areas such as healthcare. Keywords: AI, data security, ML, LLM, privacy.

Cyber Safe Harbor 4.0: Advancing Ethics and Professionalism in Indo-nesia’s Digital Landscape

The rapid development of digital platforms has prompted the need for robust policies to ensure ethical and professional conduct among platform and content providers. The "Digital or Cyber Safe Harbor Policy 4.0" is a comprehensive framework designed to protect these entities from legal liabilities while fostering a safe and innovative digital environment. This essay explores the implications of this policy within the Indonesian context, highlighting its importance, implementation strategies, potential impacts, challenges, and future improvements. It emphasizes the need for legislative updates, effective compliance mechanisms, and public awareness campaigns to ensure successful adoption. Ethical and professional issues are examined for platform and content providers, focusing on preventing misinformation, protecting user privacy, combating hate speech, maintaining transparency, and upholding accountability. The essay also discusses the necessity of balancing regulation and innovation, addressing enforcement challenges, and aligning global standards with local contexts. The conclusion underscores the critical role of the policy in supporting Indonesia's digital growth, protecting free speech, and ensuring ethical standards while advocating for continuous updates, stakeholder collaboration, enhanced user education, and infrastructure improvements to perfect the policy in the future.

Safeguarding User-Centric Privacy in Smart Homes

Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system—PrivacyGuard—that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving.

Privacy preserving large language models: ChatGPT case study based vision and framework

AbstractThe generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy‐preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre‐processing and preserving private context and the private training process for large‐scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy; specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and resource consumption. It is concluded that differential privacy, randomisation, and obfuscation can impact the training models' utility and performance; conversely, using Tor, Blockchain, and Private Information Retrieval (PIR) may introduce additional computational complexity and high training latency. It is believed that the proposed model could be used as a benchmark for privacy‐preserving LLMs for generative AI tools.

Investigating Adoption Determinants, Obstacles, and Interventions for AI Implementation in Emirati Media Organizations

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.