Latest / 1 More Paper / Ethical challenges of generative artificial intelligence in global social communications: International standards and practices; [Етичні виклики генеративного штучного інтелекту в глобальних соціальних комунікаціях: міжнародні стандарти та практики] — Ketners & Petersone (2026)
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- You’re listening to “Ethical challenges of generative artificial intelligence in global social communications: International standards and practices; [Етичні виклики генеративного штучного інтелекту в глобальних соціальних комунікаціях: міжнародні стандарти та практики],” by K. Ketners and M. Petersone. Published in 2026. Society. Document. Communication Journal homepage: the linked source Society. Document. Communication, Vol. 11, No. 2, 151-165 Ethical challenges of generative artificial intelligence in global social communications: International standards and practices Karlis Ketners Doctor of Philosophy Vytautas Magnus University Agriculture Academy LT-53361, 11 Studentų Str., Akademija, Lithuania the linked source Mara Petersone PhD in Economic Sciences, Leading Researcher Riga Technical University LV-1048, 6-A Kipsalas Str., Riga, Latvia the linked source Abstract. The purpose of the study was to analyse the global experience of forming ethical and legal frameworks for the use of generative artificial intelligence in the media industry, publishing and public communications. The research methodology included a theoretical analysis of scientific sources to identify global challenges of artificial intelligence in the media, a comparative analysis of international regulatory approaches to artificial intelligence, a systematic analysis of professional codes of ethics of journalists to assess the adaptation of traditional norms to algorithmised content distribution, and a case study of leading media organisations and platforms for investigating fact-checking practices, the use of algorithmic ranking, and the integration of ethical standards with automated systems. It was found that generative artificial intelligence automatically creates press releases, social media posts, and multimedia campaigns, which increases the efficiency of communication and the effectiveness of information impact. The development of an ethical and legal framework for the use of generative artificial intelligence in media, publishing, and public communications is accompanied by an increase in distrust of AI content (more than 60%), an increase in transparency requirements (approximately 70%), an increase in a rapid increase in deepfake content (more than 900%), which generally confirms the need for comprehensive legal regulation and control. Global cases show that tools such as British Broadcasting Corporation Verify (a division of British Broadcasting Corporation), Associated Press Content Verification Systems and Google Fact Check Explorer (Google) combine algorithmic data analysis, open intelligence sources, and automatic information filtering with mandatory human fact-checking, which allows quickly detecting misinformation and ensuring the reliability of news in real time. The practical significance of the study is that the results can be used by journalists, editorial offices, publishers, and Public Relations specialists to implement transparent algorithmic systems, ensure copyright compliance, adapt codes of ethics to digital platforms, and improve the effectiveness of content management and communication campaigns Suggested Citation: Ketners, K., & Petersone, M. (2026). Ethical challenges of generative artificial intelligence in global social communications: International standards and practices. Society. Document. Communication, 11, 151-165. doi: 10.69587/sdc/2.2026.151. Introduction. The rapid introduction of generative artificial intelligence into global social communications is transforming the ways in which information is created, disseminated, and interpreted, while simultaneously actualising the complex of ethical challenges associated with content reliability, algorithmic biases, data privacy, and responsibility for automated generation results. The growing role of such technologies in the media space, political processes, and business communications increases the risks of disinformation, manipulation, and social inequality, which leads to the need to agree on international standards and ethical principles for their use. The study of the ethical challenges of generative artificial intelligence (AI) and the analysis of international regulatory practices appears as an important area of understanding the transformations of the contemporary communication environment, focused on ensuring transparency, accountability, and safe functioning of digital technologies in a global society. The growing role of generative artificial intelligence in global social communications requires a comprehensive understanding of ethical challenges and the search for effective international approaches to their settlement. M. Al-Kfairy et al. (2024) reviewed interdisciplinary approaches to analysing the ethical problems of generative AI and examined ways to addressing them through a combination of technological and regulatory solutions. The researchers considered issues of algorithmic bias, developer responsibility, and the risks of distributing unreliable content. Expanding the scope of generative AI in complex digital environments deepens attention to the social implications of its use. B. Chimbga (2023) investigated the ethical and social challenges of integrating generative AI into the Internet of Things environment and analysed the impact of automated systems on user security. In this paper, the researcher considered the issues of confidentiality, autonomy of solutions, and potential risks of manipulative influence. Simultaneously, there is an interest in the use of generative AI in academic communication and its impact on the quality of scientific texts. K. Costa et al. (2024) examined the benefits and challenges of using generative AI in academic writing and analysed the impact of such tools on the integrity and quality of research. The risks of plagiarism, reduced critical thinking, and the need to develop recommendations for responsible use were analysed. Further generalisation of scientific approaches was carried out through the analysis of the structure of ethical discourse. G.P. Georgiou (2025) conducted thematic modelling of scientific publications and analysed key areas of generative AI research. The paper covered the topics of trust in information, transparency of algorithms, and social consequences of digital technologies. G. Kostygina et al. (2023) analysed disclosure standards in social media and generative AI research and outlined approaches to ensuring reproducibility of results. Mechanisms for increasing transparency and responsible use of data in the digital environment were identified. The development of international approaches to regulating generative AI was linked to the ideas of sustainable development and global responsibility. G.G. Landini (2025) investigated the ethical principles of using generative AI and analysed the possibilities of their integration into international policies. The relationship between technology innovation and social responsibility was traced. The regulatory dimension of the problem reflects the transformation of legal approaches to regulating digital technologies. S. Sahebi & P. Formosa (2025) investigated the phenomenon of AI-mediated communication and analysed the impact of generative systems on user epistemic trust. The risks of information distortion and transformation of its perception processes were analysed. Socially oriented approaches focus on the possibilities of generative AI for social development. W. Shafik (2025) explored the potential of generative AI in achieving the Sustainable Development Goals and analysed its role in solving global problems. The balance between the innovation of technologies and the ethical responsibility of their application was evaluated. Generalisation of scientific developments contributes to the systematisation of key ethical challenges of generative AI.E.C. Tırpan (2024) reviewed and analysed the main categories of ethical issues. The study covered issues of bias, transparency, responsibility, and impact on social processes. Methods for minimising ethical risks and improving the reliability of generative AI in social communications were analysed. Despite the questions raised by previous studies, there were still some gaps in the study of the ethical problems of generative AI. In particular, international standards and comparative regulatory practices of different jurisdictions were not sufficiently systematised. There is a lack of research that comprehensively assesses the impact of generative AI on information trust and social responsibility in global communication networks. The purpose of the study was to investigate international practices in creating ethical and legal norms for the use of generative artificial intelligence in various fields. The objectives of the study were: to compare international approaches to AI regulation (on the example of the EU AI Act, US standards and Asian models); to investigate the transformation of professional ethical codes of journalists and editors under the influence of algorithmisation; to systematise global cases of using AI to combat disinformation and create content. Materials and Methods. The research includes theoretical analysis of scientific sources, comparative analysis of international regulatory approaches to AI, evaluation of professional codes of ethics of journalists based on system analysis and case studies of leading media organisations with the study of fact-checking practices, algorithmic ranking, and integration of ethical standards in digital journalism. A theoretical analysis of the global challenges associated with the use of artificial intelligence in media, in particular, algorithmic bias, problems of copyright to content created by AI, and the transformation of the role of the “gatekeeper” in information flows based on J. Kuai (2024), M.Á. Fernández‐Barrero & C. Serrano-Martín (2025), D.V. Voinea (2025) allowed identifying key trends in the regulation and standardisation of AI tools in the media. Statistics on the level of distrust of AI content in the media, requirements for transparency of its use, legal regulation of authorship in the publishing sphere, the spread of AI-generated disinformation in public communications, and the dynamics of growth of deepfake content based on data by F. Simon et al. (2025), N. Newman (2026) were also considered. Additional sources included NewsGuard (2025), Tamzid (2026), and McAfee (2026). Within the framework of the study, a comparative analysis of international approaches to the regulation of artificial intelligence was carried out, in particular, the regulatory model of the European Union (AI Act) (Artificial Intelligence) (Proposal for a Regulation..., 2021) and the decentralised regulatory environment of the United States of America (based on the recommendations of the Federal Trade Commission (n.d.) and the National Institute of Standards and Technology. The paper also described the principle-oriented approaches of Asian countries, as set out in Japan’s Social Principles of AI (Japan AI social principles, 2025) and Singapore’s Model AI Governance Framework. The choice of these approaches was determined by their representativeness to three different regulatory paradigms of strict legal regulation, flexible industry self-regulation, and ethical governance that provides a comprehensive coverage of global practices. The selection criteria were the level of regulatory formalisation, the degree of mandatory requirements, the availability of risk classification, control, and responsibility mechanisms, and a focus on the balance between innovative development and the protection of human rights. Comparisons were made based on certain criteria that included regulatory rigidity, transparency, accountability, human oversight, institutional control organisation, and cultural approach. Four professional codes of ethics of journalists were considered through system analysis, among them Society of Professional Journalists (2014) Code of ethics, Declaration of Principles on the Conduct of Journalists (1954). The features of the RTDNA Canada (2021) Code of ethics and the Canadian Association of Journalists (2023) Code of ethics were also highlighted, with an emphasis on four key aspects, including regulatory rigidity, transparency, accountability, human oversight, institutional control organisation, and cultural approach. A case study was conducted by the following companies and organisations: British Broadcasting Corporation (BBC) Verify (BBC Verify largely..., 2025), European Broadcasting Union (EBU), Associated Press. In addition, Google (Google AI for Developers, n.d.), WAN-IFRA (The age of AI in the newsroom, 2025), and individual scenarios of protests in Los Angeles, related to the spread of disinformation and the public response to the use of AI in the media space and medical disinformation through deep fakes were analysed. BBC Verify, European Broadcasting Union, Associated Press, and WAN-IFRA were selected as leading representatives of various segments of the global media industry and demonstrate practices in implementing artificial intelligence and countering disinformation. In particular, Google was included in the analysis, as it formed the infrastructure for distributing and verifying information through AI algorithms that directly affect the media space. The study examined real-time fact-checking practices, the use of Open Source Intelligence (OSINT), data analysis, automated pre-filtering of content, the use of AI assistants to assess the credibility of news, algorithmic cross-referencing with reliable sources, the integration of ethical standards and transparency, and the combination of algorithmic systems with human fact-checking. Results. Theoretical analysis of the use of artificial intelligence in the media industry, publishing, and public communications Artificial intelligence in the media creates a number of global challenges, including algorithmic bias, copyright issues for AI-generated content, and the transformation of the role of the gatekeeper in information flows. Copyright problems were related to the fact that generative AI systems create texts, images, videos, and other content based on large amounts of data, including human works, which causes legal disputes about the ownership of copyrights to the resulting products. This requires the development of legal frameworks, licensing schemes, and standards for using data for model training. The transformation of the role of the gatekeeper is that conventional editors and journalists who previously carried out control and filtering of information flows based on professional and ethical standards are replaced by algorithmic systems that automatically select, rank, and recommend content to users, which requires revision of professional codes of ethics, integration of transparency principles, and combined human and AI control, and the development of mechanisms for monitoring algorithmic decisions. Taken together, these challenges define key areas of AI regulation and standardisation in media, including algorithm audits, transparency and accountability, copyright protection, and adaptation of ethical standards to the digital transformation of the information environment. The use of artificial intelligence in publishing and public communications is becoming increasingly important in the current conditions, although the level of systematic research remains limited and fragmented. Theoretically, AI is considered as a tool for automating content processes, optimising editorial decisions, and improving the effectiveness of communication with the audience. In the publishing industry, algorithms are used to generate texts, edit them, proofread them, automatically translate them, and adapt materials to different audience segments. Machine learning tools analyse large text arrays, identify stylistic errors, offer options for improving the structure, and predict reader preferences, which contributes to more accurate editorial policy planning. In public communications, AI is integrated into the processes of interaction with the audience through chatbots, automated user service systems, and analytical platforms for monitoring the information environment. This allows quickly responding to changes in public opinion, identifying information risks, and adjusting communication strategies. An example of practical application is algorithms for personalisation of news feeds, which adapt content in accordance with the interests of users, and automatic fact-checking systems that compare information with reliable sources and reduce the level of disinformation spread. The use of AI is accompanied by problems related to ethical aspects, risks of algorithmic bias, and the need to combine automated solutions with human control. Thus, AI in publishing and public communications forms a new paradigm of information management, where the key role is played by the integration of technological innovations with professional standards of journalism and communication, which ensures a balance between efficiency, reliability and trust of the audience. Algorithmic bias manifests itself through the use of large amounts of data reflecting historical social biases and structural inequalities in news ranking systems, content recommendations, and automatic moderation. A ProPublica study found an algorithm bias against African-American defendants. In the media, this can lead to underestimation of topics related to minorities or regional communities, and requires auditing algorithms, transparency of models, and the involvement of various social groups in their testing. Copyright issues for AI-generated content are related to the use of generative models such as OpenAI Generative Pre-trained Transformer (GPT) (OpenAI Developers, n.d.), Drawing with Artificial Intelligence – Learned Examples (DALL‐E) (OpenAI, n.d.) and MidJourney (n.d.), which train on large data sets containing people’s work. This creates legal disputes about the ownership of copyright to the resulting content. For example, in the United States in 2023, there was a legal dispute between artists and companies that used their work without consent to train AI models, which led to claims for licensing fees. Media editors use AI tools to create texts, illustrations, and totals of large amounts of data, which requires clear regulation and transparency of the use of data for model training. The transformation of the role of the gatekeeper is evident in the fact that editors and journalists are no longer the only filters of information. Algorithms for social platforms such as Facebook, YouTube and X (Twitter), automatically select and rank content for users based on interests, behaviour, and historical data. This shifts control from human to algorithm, calling into question traditional editorial standards for the accuracy, significance, and ethics of materials. Codes of ethics, such as the Society of Professional Journalists (2014), the Declaration of Principles on the Conduct of Journalists (1954) and the Canadian Association of Journalists (2023) have been supplemented with provisions on algorithm transparency, the explainability of automated decisions, and active human involvement in the oversight of AI systems. The global challenges of using AI in the media have technical, ethical, legal, and social dimensions. Algorithmic bias requires independent audits and multi‐level testing of algorithms, copyright issues stimulate the development of new legal tools for data and content protection, and changing the role of the gatekeeper leads to a revision of professional standards in favour of models that combine human control and automated mechanisms. This integrated approach allows assessing the prospects for regulating and ethical use of AI in the media and ensuring the sustainable development of information ecosystems. Table 1 summarises the key trends in the regulation and standardisation of AI tools in the media, reflecting the main areas of development of legal norms, ethical standards, and technical requirements for the use of artificial intelligence in the media environment. Source: compiled by the authors based on M.Á. Fernández‐Barrero & C. Serrano-Martín (2025), R. Saeed et al. (2026) Table 1 summarises key trends in the regulation and standardisation of AI in the media, reflecting the complexity of approaches covering technical, legal, and ethical aspects of technology application. Transparency of algorithms ensures clarity and accountability of AI decisions in ranking news and content recommendations, while copyright measures for AI-generated content contribute to resolving legal disputes and standardising the use of data for model training. The transformation of the role of the gatekeeper combines conventional editorial control with algorithmic systems, which increases the effectiveness of moderation of information flows, and ensures compliance with professional standards. Ethical standardisation and international coordination allow establishing universal principles of non-discrimination, multi-level control, and harmonisation of norms at the global level. Taken together, these trends demonstrate that the regulation and standardisation of AI in the media requires an integrated approach that combines technical, legal, and ethical mechanisms to ensure the stable, reliable and responsible functioning of the digital information environment. The experience of developing an ethical and legal framework for the use of generative artificial intelligence in the media industry is characterised by increasing requirements for transparency and reliability of information. Empirical evidence shows that more than 60% of the audience shows distrust of news content created or modified with the help of AI, which actualises the need for its clear labelling and editorial control. This situation contributes to the introduction of ethical principles of openness in the use of algorithms, responsibility of media organisations, and ensuring fact-checking as a mandatory component of journalistic practice. In publishing, the development of regulatory approaches focuses on the regulation of authorship and intellectual property issues in the context of using generative AI. In particular, approximately 65% of industry representatives support the need to create clear legal mechanisms for determining the status of content generated by algorithms, and responsibility for its distribution. This encourages the integration of academic integrity norms, the development of editing policies, and the development of hybrid models of collaboration between author and technology. In the field of public communications, the key area is countering disinformation and manipulative content created using generative AI. Studies show that the spread of false statements generated by AI reaches approximately 27% among the verified materials, reflecting a significant increase in information risks. In this regard, approaches are being formed aimed at strengthening control over information sources, developing tools for detecting artificial content, and improving the level of media literacy in society. An additional aspect is the growth of cyber threats and digital manipulations associated with the use of generative AI. It has been reported that more than 80% of modern online scams contain elements of automated content creation, which makes them more difficult to detect and increases the level of risks for users. However, the number of deepfake materials is growing by more than 900%, which increases the need for the introduction of technologies for identifying fakes and regulating their use. In response to these challenges, comprehensive ethical and legal mechanisms are being developed to ensure the safe functioning of the digital information environment. International approaches to regulating artificial intelligence International standards are developed to ensure consistency of approaches to the regulation and use of technologies in the global environment, which is especially important in the context of the development of artificial intelligence. They reduce legal uncertainty, promote international cooperation and form common rules for states and organisations, which allows effectively combining innovative development with the requirements of security and human rights protection, as demonstrated by the integrated approach of the European Union (Proposal for a Regulation..., 2021). The content of international standards includes clearly defined elements: terminology base, risk classification, principles of ethical use of technologies, requirements for transparency and accountability, and mechanisms for monitoring and assessing compliance. For example, in contemporary artificial intelligence management frameworks, it is necessary to use risk management and responsible implementation of technologies. This allows systematically assessing the impact of technologies and ensuring their safe functioning at all stages of the life cycle. The development of international standards is driven by the need to establish a balance between stimulating innovation and preventing potential risks to society. They are formed based on agreed positions of governments, the scientific community, and business, which ensure their practical applicability in different countries. However, standards can be either binding or recommendatory, but in both cases, they are aimed at increasing confidence in technologies and ensuring their responsible use. Table 2 shows a comparative analysis of key international regulatory approaches to artificial intelligence. Table 2 shows that the main difference between international standards is the approach to regulating AI. The European Union is represented by the AI Act (Proposal for a Regulation..., 2021), which sets out a strict legislative framework with mandatory requirements for high-risk systems, clear risk classification criteria, legally established liability mechanisms, and a centralised multi-level surveillance system. The process of establishing an ethical and legal framework for the use of generative artificial intelligence in the media industry, publishing and public communications is viewed as a phased regulatory process, with bans on certain high-risk practices already in place from February 2025 and basic requirements for AI literacy being introduced; from August 2025, rules for general-purpose models come into force, with an emphasis on transparency and risk management, and from 2 August 2026, requirements for high-risk systems will be fully applied, alongside mandatory conformity assessment, human oversight and the creation of national regulatory “sandboxes” in each country, which together will ensure the transition to comprehensive legal and ethical regulation of AI in the information sector. Source: compiled by the authors based on Proposal for a Regulation... (2021), AI Verify Foundation & Infocomm Media Development Authority (2024), Japan AI social principles (2025), Federal Trade Commission (n.d.) Transparency and intelligibility of algorithms, accountability of organisations, and mandatory human supervision are formalised at the legislative level, which ensures a high level of human rights protection and safe implementation of AI. The United States is adopting a flexible, decentralised model based on the recommendations of the Federal Trade Commission (n.d.), where regulatory rigidity is less, and control is carried out through agencies and corporate self-regulation practices. Transparency and accountability are rather recommended, and human oversight is not required, which allows innovating faster while maintaining market flexibility. The Asian approaches presented by Japan’s Social Principles of AI and Singapore’s Model AI Governance Framework are based on voluntary ethical principles and advisory mechanisms, focusing on the balance between technological development, social harmony, and organisational responsibility. Transparency, accountability, and human oversight in these models remain fundamental, while the institutional organisation of control is coordinating, reflecting the culturally determined values and specifics of AI implementation in different societies. Transformation of journalistic ethics under the influence of artificial intelligence In the world’s leading media and digital platforms, there is an active integration of artificial intelligence technologies into the work of editorial offices and news distribution, which requires adaptation of traditional ethical principles of journalism and the introduction of new responsibility procedures. For example, The New York Times actively encourages the use of internal AI tools for auxiliary tasks, such as preparing headlines, ideas for materials, or data research, but restricts the role of generative AI in creating the main content, leaving journalists with the final editing and confirmation of facts. Similarly, journalistic publications in the United States have begun to use AI to optimise editing and research processes, but academic reviews indicate that the use of such technologies remains uneven and their role is not disclosed to readers, creating challenges for transparency and accountability. In response to these challenges, news organisations and digital platforms are gradually developing ethical guidelines for AI tools. In particular, CBC News emphasises that artificial intelligence should remain an auxiliary, not the main content creator, with the final decision on publication and fact-checking left to people; in its principles, the editorial board requires open disclosure when AI is used in journalistic work, and an explanation of how this affected the created material. This practice reinforces the conventional ethical requirements for accuracy, transparency, and accountability laid down in professional codes and serves as a counterweight to the risks associated with automation. Algorithmic news processing in the digital environment also changes the audience’s perception of journalism: news recommendation algorithms on platforms such as social networks or content aggregators can amplify sensational content or form “information bubbles” without proper transparency about ranking criteria. Research by the BBC and the European Broadcasting Union found that about 45% of AI assistant responses contained problems with fact or sources, highlighting the need for new standards for verifying and controlling AI‐generated or AI‐curated content (Largest study of its..., 2025). In addition, the changes also affect the interaction of AI systems with conventional sources: most of the current AI models used to summarise news or make recommendations depend on available journalistic materials, but often do not provide proper citation or compensation to the original media, which raises serious ethical and legal issues. This, in turn, encourages the media to create their own policies for cooperation with AI companies, which should ensure, on the one hand, support for journalistic work, and on the other, the preservation of independence, transparency, and fairness, which are the core of professional ethics. Table 3 illustrates the transformation of professional codes of ethics for journalists and editors under the influence of content algorithmisation. Source: compiled by the authors based on Declaration of Principles on the Conduct of Journalists (1954), Society of Professional Journalists (2014), RTDNA Canada (2021), Canadian Association of Journalists (2023) The transformation of professional codes of ethics of journalists and editors under the influence of algorithmisation takes place through the adaptation of key principles of traditional journalistic ethics to the conditions of automated content processing and distribution. In global codes such as the Society of Professional Journalists (2014) Code of ethics and the Declaration of Principles on the Conduct of Journalists (1954), the principles of trustworthiness, transparency, and responsibility are detailed in relation to the use of algorithms for fact-checking, labelling AI-generated materials, and avoiding bias in automated news feeds. Simultaneously, regional standards, in particular RTDNA Canada (2021) Code of ethics and the Canadian Association of Journalists (2023) Code of ethics, include recommendations for controlling the quality of content in digital media, ensuring human supervision of algorithmically processed information, and integrating technologies to support ethical editorial decisions. Changes are also evident in practical approaches to publications: algorithmic tools are used to pre-moderate news, detect potentially harmful or biased content, and personalise news feeds, but the ultimate responsibility remains with editors and journalists. Thus, the transformation of codes consists not only in updating the texts of ethical norms, but also in introducing mechanisms of human control, automatic labelling of AI content, ethical selection and management of materials, which ensures a balance between the technological capabilities of algorithms and conventional professional standards of journalism. Global AI cases in the fight against disinformation The systematisation of global cases of using AI to combat disinformation and create content demonstrates that media organisations, technology companies and collaborative initiatives are actively implementing algorithmic solutions to identify false claims and maintain journalistic reliability in the face of rapid growth of information volumes. In particular, the practice of BBC Verify includes a special division of the British Broadcasting Corporation that uses open sources intelligence (OSINT), data analysis, and judicial methods to verify information and detect misinformation in news materials, in particular in real time through BBC Verify Live, operating on the BBC online platform, increasing audience confidence in verified facts and refuting fakes in the daily news stream (BBC Verify largely..., 2025). In partnership with other organisations and tools, journalists can automatically identify anomalies in texts and videos, enabling faster recognition of false messages during election campaigns or crisis events where speed and accuracy are crucial. The second area of AI application covers large-scale research that evaluates the impact of algorithmic systems on the way users receive news. This has led to recommendations for closer collaboration between AI developers and the media, focusing on increasing transparency, clearly differentiating facts and opinions, and regularly updating training data. Such developments encourage major news organisations, such as the Associated Press, to integrate algorithmic solutions for pre-checking videos, photos, and text that automatically filter out potentially fake content even before it gets into editorial processing. Globally, initiatives such as “News Integrity in the age of AI”, supported by the European Broadcasting Union (EBU), the World Association of news publishers (The age of AI in the newsroom, 2025) and other international media groups, call on AI developers to ensure transparent use of news materials in training models, provide clear attribution and protect the right to reliable information, and seek consistent standards for the ethical application of technology in journalism. This includes not only technical means, but also political steps, in particular, the requirement to authorise the use of content in generative models only with the consent of Tech giants like Google are also contributing to the fight against misinformation by offering tools like Google Fact Check Explorer (Google AI for Developers, n.d.), which automatically compare data sets with reliable sources such as Reuters or the Associated Press, helping journalists and consumers quickly verify news claims. Similarly, academia is working on policies and standards for using AI in fact‐checking. An example of this is a study from Germany and Ukraine that offers practical recommendations for developing regulatory approaches that promote accuracy and trust in the media environment, while minimising the risks of bias and over-automation. Individual cases demonstrate how AI is used directly in complex misinformation scenarios. During the protests in Los Angeles, AI tools were used both to distribute fake videos and messages and to automatically verify them, although the results were heterogeneous and highlighted the importance of combining AI tools and human fact‐checking. In addition, the problems of deep fakes depicting fictional doctors in the context of medical misinformation have caused a public outcry and pressure on social platforms to improve algorithmic detection of malicious content. Thus, global cases of using AI as a tool for combating disinformation demonstrate the duality of this technology: on the one hand, algorithms can accelerate the spread of false information, and on the other hand, offer powerful tools for detecting and deterring it when they are integrated into reliable journalistic processes, develop international standards for cooperation between media and technology companies, and increase user literacy in the digital environment. Global cases of using artificial intelligence in journalism demonstrate that in practice algorithmic solutions combine automatic fact checking with human control to increase the reliability of content. At BBC Verify, journalists use open sources of intelligence, algorithmic data analysis, and judicial methods to detect misinformation, including through the BBC Verify Live platform, which allows real-time verification of information, automatically identifying anomalies in texts and videos, and speeding up the refutation of fakes during election campaigns or crisis events. Such practices reflect the integration of algorithms into editorial processes, where AI helps filter potentially unreliable content before it is published, and journalists provide final verification and attribution, increasing audience confidence and compliance with ethical standards. Another area of AI use covers large-scale research on the effectiveness of automated systems and assessing their impact on news consumption. Major media organisations are implementing algorithmic tools for pre-checking videos, photos, and text, and global initiatives are calling for transparent use of news in training models, clear attribution, and the development of international standards for the ethical application of technology in journalism. The practical application of AI in complex scenarios, such as during protests or the spread of medical deep fakes, demonstrates the duality of the technology: algorithms accelerate the detection of misinformation, but simultaneously require integration with human fact‐checking to ensure accuracy and credibility of news content. Discussion. The use of generative artificial intelligence in global social communications creates new ethical challenges that require a systematic approach to regulating and standardising practices. Key issues are related to algorithm transparency, copyright, disinformation control, and responsibility for decisions made by artificial agents. This is evident in user interaction with digital platforms, where generative models act as intermediaries in the dissemination of information, content creation, and formation of public narratives. Such conclusions are consistent with the analysis of thematic models presented by H. Ali & A.F. Aysan (2025), who showed that ethical requirements for AI cover a variety of areas, from education and medicine to financial and communication systems. The integration of generative AI into social communications requires not only technological, but also normative evaluation. AI policies and guidelines remain fragmented, and their implementation varies depending on the geography and type of organisation. This is consistent with conclusions of A. Dabis & C. Csáki (2024), who examined the first policy responses of higher education institutions to the challenges of generative AI and note the lack of unified standards of ethical application. Organisations trying to implement AI policies face difficulties balancing innovation and compliance with legal norms, reflecting a general trend towards a gradual development of regulatory frameworks at the global level. The results of the study reveal the heterogeneity of AI perception in the educational environment. The use of generative models in educational processes raises discussions about the ethics of automating assessment, creating educational materials, and supporting student assignments. This coincides with the conclusions of R. AlAli & Y. Wardat (2024), who emphasised that the integration of AI into education opens up wide opportunities for personalising learning, but simultaneously creates risks of content manipulation and disruption of academic integrity. It was revealed that the element is to establish clear principles for using models, including requirements for transparency of algorithms, and the ability to track the sources of created content. Generative systems contribute to changing the role of a person in communication processes. For example, automated creation of texts, images, and multimedia content changes user interaction with information flows, affects the speed of information dissemination, and can increase the cognitive load on the audience. These results support the observation of F.F.-H. Nah et al. (2023), who described the challenges of human-AI collaboration and emphasised the need to form an ethical framework governing interaction. It turns out that without establishing rules for using generative models, the risks of social and legal consequences increase. Copyright and intellectual property issues are becoming a critical issue when applying generative models in global communications. Most of the created content does not have a clear legal attribution, which creates potential conflicts when using materials in the professional and scientific environment. This is consistent with the conclusions of N. Lucchi (2023), who investigated the ChatGPT case and innovation and limit the adoption of generative technologies in organisations. It was revealed that international standards and practices for regulating generative AI are at the stage of development, and different countries apply different approaches to ethical requirements. This reflects the conclusions of Y. Jin et al. (2025), who described a global perspective on the adoption of AI policies and recommendations in higher education institutions. International coordination, exchange of best practices and the development of unified ethical principles can reduce the risks of misuse of generative models. The use of AI in social communications requires a balance between efficiency and ethics. Technologies provide fast and large-scale access to information, and the lack of control creates risks of manipulation, dissemination of false data and reduced communication quality. This was confirmed by the conclusions of T. Farrelly & N. Baker (2023), who investigated the implications of generative AI in higher education and noted that ethical regulation is becoming necessary to maintain trust in digital communication platforms. Only through a comprehensive combination of technological, regulatory and educational approaches is it possible to ensure the effective use of generative AI. Evaluating the user experience and effectiveness of communication through AI allows identifying problems of awareness of the risks and opportunities of the technology. This was consistent with the conclusions obtained by M. Matsiola et al. (2024), who evaluated the usability of generative models in learning, ethical implications, and communication effectiveness. Increasing the digital literacy and ethical thinking of users contributes to a more informed application of generative technologies in global communications. Integrating AI into global communications can support sustainable development goals, including access to quality education. This conclusion is consistent with the study by R. Raman et al. (2024), who investigated the transformational role of generative AI in achieving the Sustainable Development Goals in education. When properly managed, generative technologies can not only improve communication efficiency, but also help reduce social inequality in access to information resources. The results show the need to update and adapt existing standards. J. Laine et al. (2025) stressed that along with conventional principles of ethics, new principles should be formed that consider the features of generative systems and their impact on society. Establishing a clear ethical framework contributes to predictability of results and reduces the risks associated with generative technologies. This confirms conclusions of R. Raman et al., who identified trends in fake news related to AI and determined the impact on achieving the Sustainable Development Goals. Generative algorithms significantly speed up the dissemination of false information, making it difficult for users to identify it. Data analysis demonstrates the existence of legal and regulatory gaps in the use of AI in global communication platforms. The results show that existing regulatory frameworks often do not cover the specifics of generative models, leaving room for abuse. This is consistent with the conclusions obtained by Y. Qian et al. (2024), who focused on the issues of ethics, law, and governance in society in the context of the active introduction of AI. The lack of clear standards increases the risks of privacy violations, and creates ambiguity in the area of responsibility for automatically created content. Users of global communication platforms demonstrate varying levels of awareness of AI ethics. This was confirmed by conclusions of R. Sabherwal & V. Grover (2024), who offered a balanced perspective on the impact of generative AI on society. According to their study, society demonstrates a simultaneous fascination with technology and concern about possible negative consequences, which coincides with the identified patterns of AI use in social networks and communication services. Generative AI creates ethical dilemmas in research and academic publications. Researchers face difficulties in determining the limits of the use of automated content and evaluating its reliability. This was confirmed by the observations of D. Schlagwein & L. Willcocks (2023), who described the ethical aspects of the use of GAI in scientific research. The use of generative models without clear ethical standards can lead to a violation of the principles of scientific integrity, and to distortion of the results of publications. The results of the study point to difficulties in balancing innovation and regulating AI on a global scale. The data show that the rapid introduction of generative technologies outstrips the development of legislative and ethical standards. X. Wang & Y.C. Wu (2024) stressed that the balance between innovation and regulatory requirements remains critical to ensuring the safe and ethical use of AI. Global regulatory practices are lagging behind the pace of technological development, creating potential risks for users and organisations. The analysis revealed the problems of managing and controlling generative AI at the international level. The lack of agreed standards creates heterogeneity in the ethical use of AI between countries and organisations. This is consistent with conclusions of A. Taeihagh (2025), who investigated the management of generative AI and emphasised the need to create global policies and frameworks that ensure the ethical use of technology. The study demonstrated that effective governance requires international coordination and clear standards of transparency and accountability. The results highlighted the privacy and data security challenges associated with AI applications. Automatically generated texts, images, and videos may pose a risk of personal information leakage or manipulation. This is consistent with the conclusions obtained by C. Uddagiri & B.V. Isunuri (2024), who considered the ethical and particular problems of generative AI. It turns out that without proper data protection and ethical protocols, the use of AI can lead to a violation of privacy and loss of user trust. The study revealed the potential of AI in industry and manufacturing processes, while pointing out the risks of misuse. Integrating generative AI into industrial systems increases efficiency, but simultaneously creates new ethical and social challenges. This is consistent with the conclusions of N.L. Rane (2023), who analysed the role of generative AI in Industry 4.0, 5.0, and Society 5.0, noting that technologies provide new opportunities for process optimisation, but also require ethical oversight to minimise negative impacts. Generative AI requires the development of guidelines for academic performance and publications that include philosophical, legal, and practical dimensions. K.-S. Tang et al. (2024), O. Le Poidevin (2025) pointed out the need for ethical and methodological recommendations for the use of AI in scientific journals. Rules for applying generative models help to avoid problems with authorship, credibility, and academic integrity. The results demonstrate the challenges of creating value through generative AI. Automatic content generation does not always bring ethical and economic benefits if there are no appropriate standards and frameworks. This coincides with the conclusions of S.F. Wamba et al. (2025), who highlighted the difficulties in creating ethical added value through generative technologies. Without ethical management and control strategies, the risks of abuse and social conflict increase. The results show that generative AI creates both new opportunities for optimising processes and communications, and ethical, social, and organisational challenges. Effective use of technology requires clear rules, ethical oversight, and integration of standards to minimise risks and ensure accountability. Conclusions. The use of generative artificial intelligence in the media industry, publishing, and PR communications creates a complex of technical, ethical, legal, and social challenges, including algorithmic bias, copyright issues for AI-generated content, and the transformation of the role of gatekeeper. In media, AI automates the creation of news and multimedia materials, increases the speed of publication preparation and content personalisation in publishing, and ensures the prompt development of releases and posts in PR communications. These processes require transparency of algorithms, audits, adaptation of codes of ethics and legal regulation for the sustainable development of digital information ecosystems. International standards for the regulation of artificial intelligence form common principles and approaches for the safe and responsible use of technologies in the global environment, ensure the harmonisation of policies and practices of various states and organisations, reduce legal uncertainty, promote transparency, accountability, and ethics of the implementation of systems, include risk classification, mechanisms for monitoring and conformity assessment, and balance innovation development with the protection of human rights, while differing in regulatory rigidity, institutional organisation, degree of human supervision and cultural approach, which allows adapting standards to the specifics of national contexts and ensures their practical applicability. The transformation of journalistic ethics under the influence of artificial intelligence consists in adapting conventional principles of reliability, transparency, and accountability to the conditions of automated content creation, processing, and distribution, which involves the development of internal media policies regarding the use of AI tools, the introduction of algorithmic labelling of materials, auditing systems to avoid bias, integrating human control into moderation and fact‐checking processes, and training editors and journalists in the ethical use of technologies, ensuring a balance between speed and personalisation of content and compliance with professional ethical standards in a global digital environment. Global cases of the use of artificial intelligence in the fight against disinformation show that algorithmic Acknowledgements. None. Funding None. Conflict of Interest None. 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Рига, Латвія the linked source Анотація. Метою дослідження був аналіз світового досвіду формування етичних та правових основ використання генеративного штучного інтелекту в медіаіндустрії, видавничій справі та публічних комунікаціях. Методологія дослідження включала теоретичний аналіз наукових джерел для виявлення глобальних проблем штучного інтелекту в медіа, порівняльний аналіз міжнародних регуляторних підходів до штучного інтелекту, систематичний аналіз професійних кодексів етики журналістів для оцінки адаптації традиційних норм до алгоритмізованого розповсюдження контенту, а також тематичне дослідження провідних медіаорганізацій та платформ для дослідження практики перевірки фактів, використання алгоритмічного ранжування та інтеграції етичних стандартів з автоматизованими системами. Було виявлено, що генеративний штучний інтелект автоматично створює прес-релізи, публікації в соціальних мережах та мультимедійні кампанії, що підвищує ефективність комунікації та результативність інформаційного впливу. Розробка етичної та правової бази для використання генеративного штучного інтелекту в медіа, видавничій справі та публічних комунікаціях супроводжується зростанням недовіри до контенту, що базується на штучному інтелекті (понад 60 %), підвищенням вимог до прозорості (приблизно 70 %), посиленням регулювання авторських прав (приблизно 65 %), збільшенням частки дезінформації, пов’язаної з штучним інтелектом (приблизно 27 %), та стрімким зростанням контенту, що базується на діпфейках (понад 900 %), що загалом підтверджує необхідність комплексного правового регулювання та контролю. Глобальні кейси показують, що такі інструменти, як British Broadcasting Corporation Verify (підрозділ British Broadcasting Corporation), Associated Press Content Verification Systems та Google Fact Check Explorer (Google), поєднують алгоритмічний аналіз даних, відкриті джерела розвідки та автоматичну фільтрацію інформації з обов’язковою перевіркою фактів людиною, що дозволяє швидко виявляти дезінформацію та забезпечувати достовірність новин у режимі реального часу. Практичне значення дослідження полягає в тому, що результати можуть бути використані журналістами, редакціями, видавцями та фахівцями зі зв’язків з громадськістю для впровадження прозорих алгоритмічних систем, забезпечення дотримання авторських прав, адаптації кодексів етики до цифрових платформ та підвищення ефективності кампаній з управління контентом та комунікацій Ключові слова: соціальні мережі; міжнародна координація; створення контенту; авторське право; гейткіпер