Generative Artificial Intelligence in Media Production. The Emerging Role of Artificial Intelligence Artist in Spain

during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access).

Response to M. Trengove & coll regarding "Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine".

Purpose This study aims to critically examine the socio-technical, economic and governance challenges emerging at the intersection of Generative artificial intelligence (AI) and Urban AI. By foregrounding the metaphor of “the moon and the ghetto” (Nelson, 1977, 2011), the issue invites contributions that interrogate the gap between technological capability and institutional justice. The purpose is to foster a multidisciplinary dialogue–spanning applied economics, public policy, AI ethics and urban governance – that can inform trustworthy, inclusive and democratically grounded AI practices. Contributors are encouraged to explore not just what GenAI can do, but for whom, how and with what consequences. Design/methodology/approach This study draws upon interdisciplinary literature from public policy, innovation studies, digital governance and urban sociology to frame the emerging governance challenges of Generative AI and Urban AI. It builds a conceptual foundation by synthesizing insights from comparative city case studies, innovation systems theory and normative policy frameworks. The approach is interpretive and exploratory, aiming to situate AI technologies within broader institutional, geopolitical and socio-economic contexts. The study invites contributions that adopt empirical, theoretical or practice-based methodologies addressing the governance of GenAI in cities and regions. Findings This study identifies a critical gap between the rapid technological advancements in Generative AI and the institutional readiness of public governance systems – particularly in urban contexts. It finds that current policy frameworks often prioritize efficiency and innovationism over democratic legitimacy, civic trust and inclusive design. Drawing on comparative global city experiences, it highlights the risk of reinforcing power asymmetries without robust accountability mechanisms. The analysis suggests that trustworthy AI is not a purely technical attribute but a political and institutional achievement, requiring participatory governance architectures and innovation systems grounded in public value and civic engagement. Research limitations/implications As an editorial introduction, this study does not present original empirical data but synthesizes key theoretical frameworks, case studies and policy debates to guide future research. Its analytical scope is conceptual and comparative, offering a foundation for submissions that further investigate Generative and Urban AI through empirical, normative and practice-based lenses. The limitations lie in its broad coverage and reliance on secondary sources. Nonetheless, it provides an agenda-setting contribution by highlighting the urgent need for interdisciplinary research into how AI reshapes public governance, institutional legitimacy and urban democratic futures. Practical implications This editorial offers a structured framework for policymakers, urban planners, technologists and public administrators to critically assess the governance of Generative and Urban AI systems. By highlighting international case studies and conceptual tools – such as public algorithmic infrastructures, civic trust frameworks and anticipatory governance – the article underscores the importance of institutional design, regulatory foresight and civic engagement. It invites practitioners to shift from techno-solutionist approaches toward inclusive, democratic and place-based AI governance. The reflections aim to support the development of trustworthy AI policies that are grounded in legitimacy, accountability and societal needs, particularly in urban and regional contexts. Social implications The editorial underscores that Generative and Urban AI systems are not socially neutral but carry significant implications for equity, representation and democratic legitimacy. These technologies risk reinforcing existing social hierarchies and systemic biases if not governed inclusively. This study calls for reimagining trust not as a technical feature but as a relational, contested dynamic between institutions and citizens. It encourages submissions that examine how AI reshapes the urban social contract, affects marginalized communities and challenges existing civic infrastructures. The goal is to promote AI governance frameworks that are pluralistic, just and reflective of diverse societal values and lived experiences. Originality/value This editorial offers a timely and conceptually grounded intervention into the emerging field of Urban AI and Generative AI governance. By framing the challenges through Richard R. Nelson’s metaphor of The Moon and the Ghetto, this study foregrounds the gap between technical capabilities and enduring societal injustices. The contribution lies in its interdisciplinary synthesis – bridging innovation systems, AI ethics, public policy and urban governance. It introduces a critical framework for assessing “trustworthy AI” not as a technical goal but as a democratic achievement and encourages research that is policy-relevant, equity-oriented and attuned to the institutional realities of AI in cities.

This paper reviews the current state of the art in artificial intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically machine learning (ML) algorithms, is provided including convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs) and deep Reinforcement Learning (DRL). We categorize creative applications into five groups, related to how AI technologies are used: (i) content creation, (ii) information analysis, (iii) content enhancement and post production workflows, (iv) information extraction and enhancement, and (v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, ML-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of ML in domains with fewer constraints, where AI is the ‘creator’, remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human-centric—where it is designed to augment, rather than replace, human creativity.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

The convergence of artificial intelligence (AI) and blockchain technology is transforming the creative economy by enabling secure, transparent, and decentralized innovation in digital content creation, intellectual property management, and monetization. Traditional creative industries are often constrained by centralized platforms, opaque copyright enforcement, and unfair revenue distribution, which limit the autonomy and financial benefits of creators. By leveraging blockchain’s immutable ledger, smart contracts, and non-fungible tokens (NFTs), digital assets can be authenticated, tokenized, and securely traded, ensuring ownership verification and automated royalty distribution. Simultaneously, AI-driven tools such as generative adversarial networks (GANs), neural networks, and natural language processing (NLP) models facilitate content generation, curation, and adaptive recommendations, enhancing creative workflows and fostering new artistic possibilities. This research report explores the synergies between AI and blockchain in the decentralized creative economy, analyzing their impact on digital rights protection, NFT marketplaces, decentralized publishing, AI-assisted music composition, and smart licensing models. Furthermore, it examines regulatory challenges, ethical considerations, and scalability limitations that need to be addressed for mainstream adoption. By integrating AI-powered automation with blockchain’s decentralized infrastructure, this study outlines a sustainable roadmap for secure, fair, and transparent digital creativity in the Web3 era. Keywords AI-powered creativity, blockchain-based digital ownership, decentralized innovation, generative AI, smart contracts, non-fungible tokens (NFTs), digital content authentication, AI-driven content generation, decentralized autonomous organizations (DAOs), intellectual property management, AI in art and music, Web3 creativity, tokenized digital assets, secure content monetization, ethical AI in blockchain, AI-assisted copyright protection, decentralized publishing, AI-powered music composition, blockchain scalability, AI for digital rights management.

Can an Artificial Intelligence (AI) Be an Author on a Medical Paper?

Innovation is a key success factor in every industry, including marketing communications. One of the most significant innovations in marketing is use of artificial intelligence (AI). Without a doubt, it has tremendous potential to become an essential content creation tool for marketing communications. In recent years, several AI-based tools have been created that simplify the content creation process in various ways. We are witnessing an enormous increase in these services and a significant increase in their quality. This qualifies them for use to a much greater extent than before. It is obvious that these tools will become key tools for marketers and related professions in the near future. Experience from the past months reveals that AI-based tools can significantly speed up content creation and increase its relevance by using optimal vocabulary, stylistics, or - in the case of image generators - visuals. The presented work analyzes the current state of using AI in marketing communication, through the possibilities and challenges related to it. The aim of this work is to provide a comprehensive overview of the possibilities of using AI in the creation of content. AI can automate some tasks, such as data analysis and information processing, freeing marketers to focus on other important tasks. The advantages of AI in the creation of marketing communication content can be speed and relevance, personalisation but also objectivity. AI in the creation of content may encounter a lack of creativity, lack of emotion and empathy. AI cannot fully understand the context in which the content is to be used, which can lead to the creation of inappropriate or ineffective content. Different types of AI tools usable in marketing communication. AI can assist in various stages of the content creation process, such as generating ideas, creating a story, personalising content. AI can use target group data such as interests, purchasing behavior, demographic information. This article discusses a contribution to research that uses qualitative and quantitative scientific methods to analyse the use of artificial intelligence (AI) in marketing and marketing communications. It highlights the uses of AI in marketing and identifies a list of selected AI tools that are considered advantageous in different areas of marketing. The conclusions of this work should provide important guidance for further research and application of AI in marketing communication, which could significantly contribute to the development of the entire mentioned field.

The artificial intelligence field has seen a surge in development, particularly after the advancement of Generative Adversarial Network (GAN) models, resulting in a diverse range of applications. The varied usage of generative models significantly enhances the importance of this domain. The primary focus of this article is the history of generative models, aiming to provide insights into how the field has evolved and to comprehend the complexities of contemporary models. The diversity in application areas and the advantages introduced by these technologies are explored in detail to facilitate a thorough understanding, with the expectation that this knowledge will expedite the emergence of new models and products. The advantages and innovative applications across sectors underscore the critical role these models play in industry. Distinguishing between traditional artificial intelligence and generative artificial intelligence, the article examines the differences. The architecture of generative models, grounded in deep learning and artificial neural networks, is compared briefly with other generative models. Lastly, the article delves into the future of artificial intelligence, addressing associated risks and proposing solutions. It concludes by emphasizing the significance of the article for new research endeavors, serving as a guiding resource for researchers navigating critical discussions in the field of generative models and artificial intelligence.

The recent development of artificial intelligence (AI) technology is bringing about changes at a faster pace and on a larger scale than any other period in human history. With technological advancements overcoming the limitations of medical AI through training with databases, AI technology has made remarkable progress since the inception of deep learning for image processing with convolutional neural networks (CNN) in 2012. The recent advancements in natural language processing (NLP) have accelerated the utilization of AI through sophisticated natural language processing, enabling machines to identify and understand data regardless of the complexity of the language. This has laid the foundation for the rapid and precise development of generative AI. In the era where generative AI is being utilized without pausing in its developmental speed, we considered the civil liability of AI in our civil law principles, taking into account the inherent characteristics of AI such as unpredictability, opacity, and the black box effect. To do this, we first examined the legal liability considering the stages of AI technology development in discussing the tort liability caused by AI. Even “Weak AI,” created by AI developers, may fall under “Gefahr,” and while not all types, some may apply to strict liability in terms of risk liability. Furthermore, while reviewing civil liability applicable to AI under fault-based and no-fault liability, we also looked at the trends in the EU comparatively. In discussing no-fault liability, particularly under the Product Liability Act, we examined the possibility and implications of applying risk liability to pharmaceutical manufacturing using generative AI technology as a representative example to overcome the limitations of the existing Product Liability Act. Humanity currently lives in an era of rapid technological development and exploding big data, enjoying numerous benefits due to these advancements. As user convenience improves and massive added value is created through technological progress, the meaning of risk liability in the realm of civil liability can gain more significance. Generative AI has already drastically reduced the costs and time required for new drug development, providing substantial profits to pharmaceutical companies. However, even if the existing Product Liability Act is applied, it may be difficult to adequately remedy the harm to victims due to the reasonable alternative possibility defense regarding design defects. In the era of generative AI, we examined the possibility of applying enhanced risk liability by assuming the case of pharmaceutical manufacturing.

The clinical process to produce innovative medications generally spans over 10 years and billions of dollars from several businesses. The strength of computational chemistry and molecular modeling has improved nevertheless the conventional medical approaches deal with several important challenges due to their high failure numbers and their inability to find good medication candidates. The pharmaceutical business benefits from AI innovation through Generative AI since it offers a huge breakthrough in research capacity. Generative AI uses deep learning architectures VAEs GANs and Transformer-based systems to enable expedited drug discovery through quick chemical design and optimal drug characteristics alongside accurate biological interactions predictions. This research analyzes Generative AI's major influence on pharmaceutical science as it quickens the processes of identifying new medications. The application of AI models helps researchers to build new chemical structures while forecasting their medication performance behaviors and deliver more strong lead compounds at a better speed than old methodologies. Generative AI leads to the production of improved medication candidates with superior efficiency and decreased toxicity according to studies from Insilico Medicine and BenevolentAI and DeepMind’s AlphaFold. AI simulations offer automated drug screening combined with cost-efficient experimental approaches which boost the success rate of clinical trials. The clinical process to produce innovative medications generally spans over 10 years and billions of dollars from several businesses. The strength of computational chemistry and molecular modeling has improved nevertheless the conventional medical approaches deal with several important challenges due to their high failure numbers and their inability to find good medication candidates. Artificial Intelligence (AI) created new study opportunities in the pharmaceutical field while Generative AI stands as a revolutionary concept change. The deep learning algorithms known as Variational Autoencoders (VAEs) Generative Adversarial Networks (GANs) along with Transformer-based models in Generative AI showcase impressive capabilities to expedite drug discovery by developing molecules swiftly and improving drug characteristics as well as accurately forecasting biological drug interactions. The material offered in this study examines Generative AI's use in pharmaceutical innovation with an emphasis on its capacity to speed up drug discovery processes. The application of AI-driven models enables scientists to generate new chemical structures which following prediction of pharmacokinetic and pharmacodynamic features results in improved optimized lead compounds development rates beyond traditional methods. Generative AI leads to the production of improved medication candidates with superior efficiency and decreased toxicity according to studies from Insilico Medicine and BenevolentAI and DeepMind’s AlphaFold. AI simulations offer automated drug screening combined with cost-efficient experimental approaches which boost the success rate of clinical trials.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Getting AI Right: Introductory Notes on AI &amp; Society

The explosive advancement of contemporary artificial intelligence (AI) technologies, typified by ChatGPT, is steering humans towards an uncontrollable trajectory to artificial general intelligence (AGI). Against the backdrop of a series of transformative breakthroughs, big tech companies such as OpenAI and Google have initiated an “AGI race” on a supranational level. As technological power becomes increasingly absolute, structural challenges may erupt with an unprecedented velocity, potentially resulting in disorderly expansion and even malignant development of AI technologies. To preserve the dignity and safety of human-beings in a brand-new AGI epoch, it is imperative to implement regulatory guidelines to limit the applications of AGI within the confines of human ethics and rules to further counteract the potential downsides. To promote the benevolent evolution of AGI, the principles of Humanism should be underscored and the connotation of Digital Humanism should be further enriched. Correspondingly, the current regulatory paradigm for generative AI may also be overhauled under the tenet of Digital Humanism to adapt to the quantum leaps and subversive shifts produced by AGI in the future. Positioned at the nexus of legal studies, computer science, and moral philosophy, this study therefore charts a course for a synthetic regulation framework of AGI under Digital Humanism.

The subject of the study is systemic changes in the methodology of design thinking, taking place under the influence of the development and spread of generative artificial intelligence (AI) technologies in design and other creative industries. The purpose of the work is: analysis of modern research on the impact of generative AI technologies on creative industries, design and on design thinking; development of a structural model of design thinking to further explore the evolution of the methodology. The following tasks are set in the article: to analyze modern scientific publications regarding the essence, structure and content of design thinking; review research on the benefits and challenges of using generative AI in design processes; to develop a model that allows identifying and describing changes in key components of the design thinking methodology arising under the influence of widespread adoption of generative AI technologies. During the research, the following methods were used: analysis and synthesis of the content of technical, economic, philosophical, linguistic, historical scientific and methodical research on the problems of forming the conceptual apparatus of the design-thinking methodology and the use of generative AI in design processes; comparative-historical, retrospective methods; structural and logical analysis. The following results were achieved: the actualized need for a comprehensive research approach to analyze the multifaceted impact of AI technologies on design; the key advantages and challenges associated with the integration of AI into creative processes are identified; a structural model of presentation of the design-thinking methodology was developed in the form of four interconnected structural layers with subsequent decomposition of each of the layers into constituent elements. The conclusions highlight the depth and multifaceted nature of the changes taking place in design and other creative industries under the influence of generative AI and need further in-depth research. The developed structural model of the design-thinking methodology allows to decompose the complex creative process to a certain extent, laying the foundation for a comprehensive analysis of the evolution of the methodology and the systematic introduction of generative artificial intelligence technologies into design processes.