ai在创意领域中的争议案例研究

The emancipatory effect of copyright on the lives of creators has long been hindered by the concentration of rights by powerful entities that can hold creativity hostage through exclusive rights over countless cultural references. Exceptions to copyright have played an important counter-hegemonic role, supporting what we might call a public domain counterprinciple. The recent explosion of generative artificial intelligence (GenAI) upends this scenario, with creators bringing copyright infringement claims to the courts to determine, inter alia, the existence and relevance of copying in the training process and whether AI outputs qualify as derivative works. Using digital art as an example, this article assesses these dynamics from the perspective of emancipation, considering the interplay of copyright rules, exceptions, principles and counterprinciples, and seeks to devise pathways, within and outside copyright, to address the challenges posed to creators by GenAI.

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The discussion of AI copyright infringement or fair use often skips over all the required steps of the infringement analysis in order to focus on the most intriguing question, “Could a visual generative AI generate a work that potentially infringes a preexisting copyrighted work?” and then the discussion skips further ahead to, “Would the AI have a fair use defense, most likely under the transformative test?” These are relevant questions, but without considering the actual steps of the copyright infringement analysis, the discussion is misleading or even irrelevant. This neglecting of topics and stages of the infringement analysis fails to direct our attention to a properly accused party or entity whose actions prompt the question. Making a sudden transition from a question of infringement in the creation of training datasets to the creation of foundation models that draw from the training data to the actual operation of the generative AI system to produce images makes a false equivalency regarding the processes themselves and the persons responsible for them. The questions ought to shift focus from the persons compiling the training dataset used to train the AI system and the designers and creators of the AI system itself to the end users of the AI system who conceive of and cause the creation of images. The analysis of infringement or fair use in the generative AI context has suffered from widespread misunderstanding concerning the generative AI processes and the control and authorship of the end-user. Claimants, commentators, and regulators have made incorrect assumptions and inaccurate simplifications concerning the process, which I refer to as the Magic File Drawer theory, the Magic Copy Machine theory, and the Magic Box Artist theory. These theories, if they were true, would be much easier to envision and understand than the actual science and technology that goes into the creation and operation of a contemporary visual generative AI system. Throughout this Article, I will attempt to clarify and correct the understanding of the science and technology of the generative AI processes and explain the different roles of the training dataset designers, the generative AI system designers, and the end-users in the rendering of visual works by a generative AI system. Part II will discuss the requirements of a claim of copyright infringement including each step from the copyrightability of the claimant’s work, the doctrines that limit copyrightability, the requirement of an act of copying, and the infringement elements. Part III will summarize the copyright fair use test paying particular attention to the purpose and character of the use analysis, 17 U.S.C. § 107(1), and the current interpretation of the “transformative” test after Andy Warhol Foundation v. Goldsmith, particularly in circumstances relating to technology and the use of copyrighted or copyrightable data sources. Part IV will analyze potential infringement or fair use by the creators of generative AI training datasets. Part V will analyze potential infringement or fair use by the creators of visual generative AI systems. Part VI will analyze potential infringement or fair use by the end-users of visual generative AI systems. For all their complexity, visual generative AI systems are tools that depend on an end-user who conceives of and designs the image and provides the system with a prompt to set the generative process in motion. The end-users are responsible for crafting the prompt or series of prompts used, for evaluating the outputs of the generative AI, for adjusting and editing the iterations of images offered by the AI system, and ultimately for selecting and adopting one of the images generated by the AI as the final image. The end-users then make further decisions about the actual use and its function and purpose for the images the end-users selected and adopted from the outputs of the AI. While working with the AI tool to try to produce a certain image, an end-user might steer the system to produce a work that could, under an infringement analysis, be regarded as potentially infringing, which would lead us again to the fair use analysis based on the end-user’s use of the image.

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While different countries vary in their determination of copyrightability, jurisdictions like the United States currently do not allow an artist to copyright AI-generated content when they do not have creative control. One avenue for an author to support their case for copyright protections over work created with AI may then be to demonstrate their intent to “predict” outputs of the generative AI tool during the creation process, shifting elements of randomness from the AI to the human’s own decision-making as much as possible. When this happens, the artist might claim to have expressed their idea with generative AI, and seek copyright protection for their work. We propose that generative AI co-creation tools can support this intention by keeping records of the predictability statistics at each generative AI iteration, and capturing the potential alternate options that can be later assessed for how predictably they matched the prompt.

Recently, the advancement of generative AI technology and the rise of image-centered SNS platforms have transformed the painting-based art environment. These changes challenge traditional practices of creation and distribution and affect the overall art ecosystem. While previous studies focused on copyright and technology adoption, few addressed structural or practical issues in real-world art contexts. This study analyzes the impact of generative AI images and digital distribution on painting-centered art from the perspective of practitioners. The research involved a review of literature, statistical data, and policy reports related to generative AI and SNS-based image platforms. Quantitative analysis was conducted using the 2024 Internet Usage Survey and KOSIS data. In-depth interviews with painters, appraisers, and critics were also conducted to examine field perceptions of generative image expansion. Results showed weakened authorship, opaque distribution, and reduced authenticity in appreciation. Key problems included copyright gaps, lack of platform oversight, and diminished user discernment. Practitioners suggested legal definitions, mandatory metadata labeling, stronger regulation, and media literacy education. This study concludes with policy implications and future research directions.

Since its introduction in 2022, Generative AI has significantly impacted the art world, from winning state art fairs to creating complex videos from simple prompts. Amid this renaissance, a pivotal issue emerges: should users of Generative AI be recognized as authors eligible for copyright protection? The Copyright Office, in its March 2023 Guidance, argues against this notion. By comparing the prompts to clients' instructions for commissioned art, the Office denies users authorship due to their limited role in the creative process. This Article challenges this viewpoint and advocates for the recognition of Generative AI users who incorporate these tools into their creative endeavors. It argues that the current policy fails to consider the intricate and dynamic interaction between Generative AI users and the models, where users actively influence the output through a process of adjustment, refinement, selection, and arrangement. Rather than dismissing the contributions generated by AI, this Article suggests a simplified and streamlined registration process that acknowledges the role of AI in creation. This approach not only aligns with the constitutional goal of promoting the progress of science and useful arts but also encourages public engagement in the creative process, which contributes to the pool of training data for AI. Moreover, it advocates for a flexible framework that evolves alongside technological advancements while ensuring safety and public interest. In conclusion, by examining text-to-image generators and addressing misconceptions about Generative AI and user interaction, this Article calls for a regulatory framework that adapts to technological developments and safeguards public interests

Art inspires emotion. The process of creation invigorates the senses and keeps the researcher on fire. To create artistic masterpieces using words through natural language processing further stimulates the mind and helps to overcome writer’s block. An AI tool interprets the words and transforms them into artistic expressions that feel like magic. Yet, AI art is not without consequences. Critics challenge the digital rights of AI-generated images, noting that they are derivative works. This paper examines the joy of creation as well as the copyright challenges creators face when writing detailed prompts to generate amazing artwork. The role of affective computing illustrates the relationship between the designer and the AI tool while ethical concerns remind designers to use caution in their prompts. The article examines the architecture of a prompt, the characteristics to enhance it, and examples of generative AI art. Additional content includes references to handouts, art catalogs, and legal opinions regarding the copyright debate. The article concludes with a recommendation with respect to the intellectual property rights of artists and their creative work.

Incorporating generative artificial intelligence (AI) into design and art has upended established creative paradigms, sparking discussions on the validity of AI-generated art and the development of non-fungible token (NFT) marketplaces. The US Copyright Office rendered a significant decision in February 2023 that highlights the contentious nature of AI work and the need of human intervention in its commercialization. This paper traces the development of artificial intelligence in neural networks and examines how it has affected visual arts. We investigate the idea of autonomously creating digital art in the NFT style utilizing generative adversarial networks (GANs), with striking results. Our work links deep learning and blockchain, enabling AI to find a place in the digital art market.

This review explores the relationship between Ghibli-style visual characteristics, copyright regulations, and viral phenomena in the context of modern generative artificial intelligence (AI). This research examines the ethical, legal, and technological implications of the new wave of Studio Ghibli-inspired AI-generated visuals, focusing on their popularity. The research employed a narrative review method to synthesize findings from computer science, law, media studies, and art theory literature. Research shows that Ghibli-style content created by AI raises important copyright challenges while posing considerable ethical questions about ownership, creator identification, and the pressure on computational systems due to its widespread popularity. The paper examines theoretical explanations that link authorship with labor, as well as nostalgia dynamics that activate digital virality. The review proposes future research approaches and advocates for modern legal structures, moral guidelines, and collective leadership mechanisms to safeguard artistic quality in our age of automated creative production. Keywords: Ghibli-style, AI-generated art, copyright, viral imagery, generative AI, ChatGPT, GPU overload, digital aesthetics, intellectual property, ethical AI

The rapid advancement of artificial intelligence (AI) technology has introduced various innovations and debates within the creative industries, as it has in many other fields. In the music sector, generative AI (GAI) technologies have opened new avenues for both producers and listeners, providing novel experiences. One of the most notable developments in this regard is AI covers, which rely on voice cloning technology. GAI models can replicate an artist’s vocal style and musical characteristics. However, this emerging practice has sparked significant discussions, not only regarding copyright infringement but also concerning the violation of moral rights. This study aims to analyze the controversies surrounding AI covers, focusing not only on the production of such content but also on its consumption. The study employs thematic analysis to evaluate listener comments on the most popular Turkish AI covers available on YouTube. The findings reveal that, in addition to supportive reactions, listeners assess these works through the lens of ethical ambiguities, respect for the artist’s legacy, and copyright concerns. In this context, the study emphasizes that the future of GAI in the music industry should not be determined solely by technological advancements but must also take ethical and legal considerations into account. Using Braun and Clarke’s thematic analysis approach, the study codes the listener discussions under five highly engaged Turkish AI-cover videos and organizes them into seven themes, including copyright, ethical concerns, AI perception, artistic value, and supportive or critical reactions. The results indicate that audiences simultaneously celebrate AI covers as creative, nostalgic remix practices and problematize them as threats to artistic authenticity, consent, and fair remuneration. By foregrounding listener perspectives in a non-Anglophone context, the article offers empirical input to debates on participatory culture, musical deepfakes, and AI ethics in the music industry.

Generative artificial intelligence (AI) challenges copyright by raising questions about whether AI-generated content (AIGC) qualifies as protectable works and who should own the associated rights. The Chinese judiciary addressed these issues in Li Yunkai v Liu Yuanchun (Li v Liu), a landmark case where the Beijing Internet Court ruled that an AI-generated image qualified as a work of fine art, with authorship vested in the user who entered the original prompts. This article evaluates the validity of the first holding within the judicial narrative about generative AIs. It argues that the Chinese judiciary maintains a policy to treat generative AIs as ordinary tools for creation. On this basis, while protecting AI-generated images as works of fine art aligns with China’s copyright system, the legal rationale must derive from artistic narratives of originality and aesthetics rather than the court’s reasoning in Li v Liu. Specifically, China should adopt a zero-originality threshold for works of fine art, recognising aesthetic effects as sufficient for protection, provided human intervention occurs through the refinement of AI outputs.

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Recent advances in generative AI have significantly expanded into the realms of art and music. This development has opened up a vast realm of possibilities, pushing the boundaries of human creativity into unexplored frontiers. However, as generative AI advances, it can replicate artistic styles and produce new artwork, posing significant concerns for the perceived rarity and value of artists' creations. In response to these challenges, it is becoming increasingly crucial to establish and enforce protective measures that safeguard artists' copyrighted work from unauthorized exploitation by generative AI models. In this paper, we introduce the first defensive mechanism, HARMONYCLOAK, to prevent the exploitative use of artwork, specifically in the context of instrumental music, by generative AI models. Particularly, HARMONYCLOAK employs imperceptible error-minimizing noise to make the model's generative loss approach zero for these perturbed music data, tricking the model into believing nothing can be learned so as to disrupt their attempts to replicate musical structures and styles. By using a set of intra-track and inter-track objective metrics and a subjective user study, extensive experiments on three state-of-the-art music generative AI models (i.e., MuseGAN, SymphonyNet, and MusicLM) validate the effectiveness and applicability of Harmonycloak1.1.Audio examples of the unlearnable music examples are available for listening at https://mosis.eecs.utk.edu/harmonycloak.html. in both white-box and black-box settings.

Breakthroughs in generative AI (GenAI) have fueled debates concerning the artistic and legal status of AI-generated creations. We investigate laypeople’s perceptions (N = 432) of AI-generated art through the lens of copyright law. We study lay judgments of GenAI images concerning several copyright-related factors and capture people’s opinions of who should be the authors and rights-holders of AI-generated images. To do so, we held an incentivized AI art competition in which some participants used a GenAI model to create art while others evaluated these images. We find that participants believe creativity and effort, but not skills, are needed to create AI-generated art. Participants were most likely to attribute authorship and copyright to the AI model’s users and to the artists whose creations were used for training. We find evidence of egocentric effects: participants favored their own art with respect to quality, creativity, and effort—particularly when these assessments determined real monetary awards.

This research discusses the development of an application to test the similarity between AI Generative images and handmade images using deep learning methods. Artificial Intelligence (AI) technology has been applied to generative art through deep learning algorithms; however, there are still challenges related to copyright and originality of AI Generative art. The aim of this research is to develop an efficient model for classifying AI Generative art and handmade art. The classification model uses a Transformer approach, specifically exploiting the BEiT architecture, which shows highly satisfactory results in image classification tests. The high F1 score in each test reflects a good balance between precision and recall. The Transformer model outperforms previous methods using Convolutional Neural Network (CNN) and VGG16 models. It is expected that this model will be able to classify art more efficiently, assist in the detection of misuse, and mitigate legal risks related to copyright.

The rapid integration of Generative Artificial Intelligence (GenAI) into the screen industries is challenging long-held notions of creativity, authorship, and artistic ownership. This paper explores how GenAI tools—ranging from script-writing assistants to visual generators and voice synthesis technologies—are reshaping creative workflows in cinema, television, and digital content production. Drawing on interdisciplinary frameworks from media studies, authorship theory, and AI ethics, this study critically examines the evolving role of the human creator in an age where machines can mimic and co-create narrative structures, visual aesthetics, and character arcs. Through interviews with industry professionals, content creators, and AI developers, as well as textual analysis of AI-generated screen content, the research reveals a growing trend toward hybrid authorship models, where human intention and algorithmic suggestion coalesce. The results highlight key transformations: (1) GenAI is reducing production costs and timelines but raising questions about originality and creative control; (2) traditional screenwriters and directors are negotiating new roles as curators and collaborators of machine-generated content; and (3) industry policies and copyright frameworks are lagging behind, leading to legal ambiguities surrounding intellectual property rights. While GenAI democratizes access to content creation, it also risks homogenizing narrative structures due to data-trained biases. Ultimately, this paper argues for a redefinition of authorship in the screen industries—one that recognizes the collaborative entanglement of human vision and machine logic. As screen culture moves deeper into the algorithmic age, understanding this transformation is vital for ethical innovation and equitable recognition of creative labor.

The rise of generative AI is transforming the landscape of digital imagery, and exerting a significant influence on online creative communities. This has led to the emergence of AI-Generated Content (AIGC) social platforms, such as Civitai. These distinctive social platforms allow users to build and share their own generative AI models, thereby enhancing the potential for more diverse artistic expression. They also provide artists with the means to showcase their creations (generated from the models), engage in discussions, and obtain feedback, thus nurturing a sense of community. Yet, this openness also raises concerns about the abuse of such platforms, e.g., using models to disseminate deceptive deepfakes or infringe upon copyrights. To explore this, we conduct the first comprehensive empirical study of an AIGC social platform, focusing on its use for generating abusive content. As an exemplar, we construct a comprehensive dataset covering Civitai, the largest available AIGC social platform. Based on this dataset of 87K models and 2M images, we explore the characteristics of content and discuss strategies for moderation to better govern these platforms.

The multi-year international project ‘Illuminating the Non-Representable’ (IN-R) sought to consider the breadth of possibilities in contemporary illustration practice. A question was how, in an ever-more global context, illustration might sensitively communicate concepts of ‘the other’ – meaning persons who do not share heritage or characteristics of the perceived audience or illustrators themselves. With growing AI image generation in 2022, new questions arose regarding types of AI images users were prompting and whether resulting images perpetuate bias inherited through machine learning that is trained on databases already proven to encode bias. This article shares an analysis of a sampling of AI-generated images in response to those questions and includes expert opinions on the benefits and limits of AI creativity, ethical issues related to plagiarism and the unauthorized scraping of copyrighted works into training databases, as well as more generally on the erosion of professional practice that generative AI portends.

The integration of generative artificial intelligence (AI) tools in art and design has disrupted the traditional creative landscape, leading to debates on the legitimacy of AI-generated art and the emergence of new markets such as non-fungible tokens (NFTs). The US Copyright Office’s February 21, 2023, ruling withdrawing copyright protection for AI-generated comic artwork, while protecting the accompanying text and arrangement, highlights the contested nature of AI art and suggests that significant human intervention in the creative process will be required for monetization. Whether considered content interpolation or content creation, AI generative content for the creation of art and design is here with human-AI collaboration. To explore the potential of AI tools in creative practice, this study introduced students in a digital art course to Craiyon and Midjourney generative AI tools, with DALL-E 2 selected as the primary tool due to its varied output. The students were tasked with selecting a preferred prompt from one tool and then reproducing the output from both tools. The results revealed significant variations in replicating the outputs of different AI tools and limited exploration of prompt engineering, leading to restrictions in the iterative process of artmaking. The students agreed that generative AI tools are not a substitute for human creativity and should be used for final projects. The study demonstrates the potential and limitations of integrating AI tools in art and design and suggests the need for further research in developing effective prompt engineering strategies.

The proliferation of advanced text-to-image generative AI represents a paradigmatic shift in visual culture. It instigates a profound crisis for established concepts of authorship and aesthetics while also raising critical questions about artistic labor and the political economy of cultural production. This study investigates the complex negotiations between human creators and algorithmic systems. This study employed a qualitative, multi-modal methodology. A visual semiotic analysis was conducted on a curated corpus of 300 artworks from Midjourney, DALL-E 3, and Stable Diffusion, sampled to mitigate platform-specific biases. This was triangulated with a thematic analysis of semi-structured interviews with a purposive sample of 15 artists and designers actively using these tools. The methodological limitations, specifically the sample's "adopter-centric" bias, are explicitly acknowledged. The visual analysis identified a distinct "algorithmic gaze" characterized by hyper-compositing, surreal corporeal logic, and stylistic convergence, reflecting both the system's non-human perspective and the biases of its training data. The thematic analysis of artist interviews revealed three dominant experiential themes: the artist's role being reframed as curatorial, the creative process as a form of dialogue, and the interaction as an exploration of the system's "latent space". These participant narratives often frame the interaction in terms of empowerment and collaboration. In conclusion, generative AI reconfigures authorship into a distributed network phenomenon. However, this study argues that this posthuman collaboration occurs within a system structured by significant power asymmetries. The aesthetics of the algorithmic gaze are not neutral but are shaped by the commercial and ideological imperatives of the platforms. The artist's experience of empowerment coexists with broader material processes of deskilling, alienation, and the centralization of cultural production. Understanding this new paradigm requires a critical synthesis of posthumanist theory and political economy.

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This study aims to test whether artists and technologists evaluate artificial intelligence (AI)-generated art differently with respect to authorship, ownership, ethics, public perception and market impact, thereby clarifying how creative and technical stakeholders view emerging copyright and pricing debates. A closed-ended questionnaire using five-point Likert items was administered to 119 master-level artists and 119 AI developers in India. Paired-sample t-tests compared the two groups across six constructs derived from the recent literature. Authorship and market-impact scores did not differ significantly (p = 0.229; p = 0.168). However, large gaps emerged for ownership (Δ = 1.02; t = 15.31; p < 0.001) and public perception (Δ = 1.24; t = 11.25; p < 0.001). Artists favored limited copyright and lower prices for AI art, whereas technologists endorsed full protection and price parity under defined conditions. Both groups supported minimal yet dedicated regulation. Findings are based on self-reported perceptions from a single country; cross-cultural replication is needed. Results inform policymakers drafting adaptive copyright frameworks and gallery curators setting valuation benchmarks for AI-generated art. Balanced regulation and weighted authorship could preserve human creativity while legitimizing algorithmic contributions. This is the first paired statistical investigation that juxtaposes creative practitioners and AI developers, quantifies their attitudinal distance and links the results to the legal triad of fixation, originality and creativity. The Global South sample broadens a debate dominated by Western case studies.

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This critical incident highlights the use of generative AI in creative content industries and consumer reaction to the use of generative AI to create content. This critical incident introduces readers to Wizards of the Coast (WotC), a subsidiary of Hasbro that produces the popular tabletop role-playing game Dungeons and Dragons (D&D), and a generative AI controversy over one of its D&D sourcebooks that played out in August 2023. D&D gamers took to social media to voice their displeasure with WotC allegedly using generative AI to create artwork in a sourcebook. Cynthia Williams, President of Hasbro’s Wizards of the Coast and Digital Gaming division, and her team had to consider a response to customers' concerns and whether WotC should develop a policy to guide the use of generative AI by artists and employees. The case asks students to consider a company’s use of generative AI and the potential reaction by consumers, develop a response to public backlash regarding the use of generative AI, and plan an acceptable use of generative AI policy for a business within a creative industry.

The distribution of authorship in the age of machine learning or artificial intelligence (AI) suggests a taxonomic system that places art objects along a spectrum in terms of authorship: from pure human creation, which draws directly from the interior world of affect, emotions and ideas, through to co-evolved works created with tools and collective production and finally to works that are largely devoid of human involvement. Human and machine production can be distinguished in terms of motivation, with human production being driven by consciousness and the processing of subjective experience and machinic production being driven by algorithms and the processing of data. However, the expansion of AI entangles the artist in ever more complex webs of production and dissemination, whereby the boundaries between the work of the artist and the work of the networked technologies are increasingly distributed and obscured. From this perspective, AI-generated works are not solely the products of an independent machinic agency but operate in the middle of the spectrum of authorship between human and machine, as they are the consequences of a highly distributed model of production that sit across the algorithms and the underlying information systems and data that support them and the artists who both contribute and extract value. This highly distributed state further transforms the role of the artist from the creator of objects containing aesthetic and conceptual potential to the translator and curator of such objects.

The creativity, ownership and ethics of the art ecosystem has been re-defined in a manner that has never before been witnessed due to the fast changing nature of the artificial intelligence (AI) inside the art ecosystem. The problem that the ethics of management in this new domain deals with is the balancing of innovation and moral responsibility, to achieve transparency, fairness, and respect of the human beings and creative integrity. Ethical management should encompass the issue of authorship, intellectual property and accountability in instances whereby the art pieces generated by the AI system are raising issues of originality. The AI application in art production or curation by organizations is met with the issues of algorithmic bias, cultural appropriation, and relegation of human artists. Effective management operations should be inclusiveness, approval and equitable allocation of credit to contributors of human and machine resources. Additionally, ethical leadership should be attentive to data integrity as well as not to reproduce copyrighted material without permission. Transparency in the use of AI tools and the creative process is the key to the assurance of trust and integrity of the people to the art. Making its way through the socio-economic consequences, including the labor migration, and the commercialization of AI art, the management also should. By establishing robust ethical norms and building accountability, managers would have an opportunity to develop a sustainable, human-based innovation that would appreciate technological growth and artistic heritage within the dynamic AI-art ecosystem.

This article considers current advances in tattooing that are challenging community-held views of authorship and ownership, and the need to address this tension. The key challenge is from AI-generated artworks being used as tattoo designs, but the authorial role of the tattooist is also challenged by body art projects such as tattoo collection. Legal clarity for tattooing is lacking, and in addressing this, this article advocates for an open, community-based form of shared copyright ownership and authorship for projects as tattoo collecting, drawing on Dusollier’s and Mendis’ work. This article contributes to both copyright and cultural heritage legal scholarship, and to tattooing scholarship and the tattooing community. AI-generated art being tattooed on people has not been explored in the literature to date, and this article fills this gap. Furthermore, this article contributes a pilot study of the tattoo community’s views on AI-generated tattoos, which is currently lacking from the scholarly debate on AI-generated art. This article argues that the debate within the tattoo community about AI-generated art being used in tattoos needs to be addressed within the community through agreed extra-legal norms, which may well depart from how copyright law decides to approach AI-generated art globally. This article also asserts that AI should not be regarded as the “author” of tattoo works in the traditional copyright sense, as only a human tattooist can draw from a number of cultural, textual, audiovisual and visual, cultural folklore, history and mythical references in creating their tattoo designs, as well as drawing on the client’s personal stories. This article explores the following: (i) an understanding of tattooing as an artform; (ii) tattoos in UK copyright law; (iii) an exploration of the authorial role of the tattooist within tattooing; (iv) the authorial role of the tattooist within tattoo collecting; (v) AI-generated tattoos—perspectives from the tattoo community, through a pilot study of YouTube videos and viewer comments about this; and (vi) a consideration of whether copyright legal reform is the solution for the tattooing community.

The aim of this theoretical study was to examine the ethical dilemmas related to the use of generative artificial intelligence (AI) in visual art, with a focus on authorship, ownership, and the shifting boundaries of creativity. The research was conducted in 2024–5 using methods of conceptual analysis, legal comparison, and interdisciplinary synthesis. A total of forty-three peer-reviewed sources were analysed across the fields of art theory, cultural philosophy, intellectual property law, and cognitive aesthetics. The results show that authorship in AI-generated art is increasingly viewed as distributed. While primary responsibility is attributed to human users—through prompt design, selection, and interpretation—there is growing recognition of algorithmic systems as co-contributors. Legal frameworks in six jurisdictions (Canada, the UK, the USA, Germany, Japan, and China) remain fragmented: most require human input for copyright protection, yet differ in how they define ‘authorship’. Analysis of empirical findings from existing studies confirms that audience perception is shaped by authorship attribution: artworks known to be created by humans received significantly higher aesthetic ratings (mean = 5.72, AI = 4.99, P < .001). At the same time, the perceived originality of AI outputs led to divided judgements about their legal and cultural legitimacy. In conclusion, the study highlights that AI enhances formal production but lacks intentionality, emotion, and cultural awareness. These findings are relevant for updating legal definitions, designing transparent AI tools, and creating educational programs on human–AI collaboration in creative fields.

This paper explores the integration of Artificial Intelligence (AI) into the fine arts, highlighting its transformative role in redefining creativity, authorship, and art consumption in the digital age. Through case studies such as Emily’s Forest, the Da Vinci Genius Exhibition, and AI-driven platforms like Artivive, the paper examines how AI acts not merely as a tool but as a creative collaborator, pushing the boundaries of artistic expression. It discusses the shift in the artist's role from creator to curator, as seen in AI-generated artworks like Edmond de Belamy and music compositions by Taryn Southern, raising important questions about authorship and originality. The paper further delves into the ethical challenges surrounding AI-generated art, including issues of intellectual property and the commodification of creativity. Additionally, it explores AI’s impact on art appreciation and curation, emphasizing personalized, immersive experiences and the use of AI in preserving and restoring cultural heritage. In conclusion, while AI prompts philosophical debates and ethical considerations, it offers unprecedented opportunities for innovation in art creation, exhibition, and cultural preservation, making it a powerful force in the future of the fine arts.

This paper examines the unprecedented challenges posed by Generative Artificial Intelligence (AI-G) to copyright law, specifically within the Albanian legal framework (Law No. 35/2016). It aims to identify legal gaps regarding authorship, ownership, and economic compensation for human creators in the music and visual arts sectors.The study employs a doctrinal and comparative legal analysis, juxtaposing Albania’s legislation with US Copyright Office policies, the EU AI Act (2024), and the DSM Directive. Through context-specific case studies, the research evaluates the adequacy of current norms in the face of autonomous digital production and “style cloning.” The analysis confirms that while human authorship remains a non-negotiable prerequisite for protection, the lack of regulation for AI training data and autonomous outputs creates significant legal uncertainty. While the study proposes sui generis rights as a potential solution, it critically addresses scholarly concerns regarding the complexity and fragmentation such rights might introduce to traditional copyright doctrine.This research provides a novel, Albania-specific analysis of AI copyright implications, offering a hybrid regulatory model that balances technological innovation with the protection of national cultural identity and creators’ economic rights.The recommendations—including mandatory collective licensing, transparency requirements, and alignment with EU standards—serve as a strategic roadmap for Albanian policymakers to foster a fair digital economy and protect intellectual labor from AI-driven displacement.

Artificial Intelligence (AI) has quickly changed the artistic production by allowing machines to produce images, music, literature, and multimedia works that mimic the work of humans with regard to creativity. The latest developments of machine learning, especially deep neural networks, Generative Adversarial Networks (GANs), and diffusion-based models, have increased what computational systems can do: creating complex artistic patterns based on massive data. Such developments have also brought up critical theoretical, legal, and philosophical issues of authorship, originality, and creative ownership on AI-generated artworks. This paper looks at the technical underlying principals of AI generated art and discusses the processes by which algorithms discover stylistic tropes, generate visual shapes and respond to human intervention in user prompts and parameter adjustment. The paper also discusses the changing argument over authorship in AI-generated art, which takes into account programmers, dataset curators, artists, and end users advantages in the creative pipeline. Moral and cultural considerations are also outlined, such as the issues concerning intellectual property, cultural biasness in training data, and the possible repercussion to the conventional artistic careers. With the combination of the views of computational creativity, the digital humanities and the cultural policy, the study points to the transformative paradigm of human-intelligent systems collaborativity of creativity.

The article analyzes the question of authorship of AI-generated works in the artistic domain. As usage of AI is projected to increase in many different industries, sometimes regardless of presence of any tangible benefits to the user, we have to evaluate potential impact of an AI on the artistic domain, mostly from the perspective of it being used as a tool by various artists. Discussion is held from the perspective of the evaluation of actual work done and the quality of the result, accounting for the time spent to achieve that result, along with human-made edits where necessary. Discussions about legal standards and decisions of determining sufficient authorship of the artistic work are largely outside of the scope of this article, however, the decision by the U.S. Copyright Office is used as an example of a sufficiently good middle ground found as an attempt to acknowledge existence and importance of AI-generated art, while avoiding any decisions that could prevent its use in both commercial and non-profit environments.

Purpose This paper aims to examine the legal and ethical challenges posed by the rise of AI-generated art, focusing on issues of copyright, authorship, and creativity. By analyzing legal precedents, such as Thaler v. US Copyright Office, and the broader implications of generative AI platforms, it explores how current laws fail to address the complexities of machine-created works. The paper explores the societal and philosophical impact of AI art and provide insights for policymakers, artists, and technologists as they navigate this evolving landscape. Design/methodology/approach The design adopted in this paper is content review. Findings The paper identifies significant gaps in copyright law regarding AI-generated art, particularly around the Human Authorship Requirement. It highlights ongoing lawsuits, such as those against Midjourney and Google, which underscore unresolved legal ambiguities. The findings reveal that AI-generated art challenges traditional notions of creativity and authorship, raising ethical concerns about the use of human-created works as training data. The study concludes that legal frameworks must evolve to balance the protection of human creators with the realities of AI’s capabilities, while society must grapple with redefining artistic creativity in the age of automation. Research limitations/implications This research is limited by its reliance on existing legal cases and secondary data, as the fast-evolving nature of generative AI and its legal implications precludes comprehensive real-time analysis. Furthermore, it focuses primarily on U.S. copyright law, which may not generalize globally. However, the implications are far-reaching, calling for further interdisciplinary studies on the intersection of AI, ethics and intellectual property law. Policymakers and stakeholders are encouraged to use these findings as a foundation for crafting flexible and inclusive legal frameworks that address the complexities of AI-generated content. Practical implications This paper underscores the urgent need for legal frameworks to adapt to the realities of AI-generated content. It provides actionable insights for policymakers and legal practitioners, outlining potential pathways to update copyright laws to address the challenges posed by generative AI. The discussion of data poisoning and training datasets offers practical solutions for artists seeking to protect their works. Additionally, it highlights the responsibilities of AI developers to implement ethical practices in dataset creation and user guidance, ensuring a balanced approach to fostering innovation while respecting intellectual property. Social implications The widespread accessibility of AI art platforms has democratized creative expression but also disrupted traditional notions of artistry and authorship. This paper explores the societal impact of this disruption, including the erosion of the perceived value of human artistic craftsmanship and the philosophical challenge to human uniqueness in creative endeavors. It emphasizes the ethical concerns raised by artists over the unauthorized use of their works and stresses the importance of fostering societal dialogue to navigate these tensions. The findings encourage balanced integration of AI into creative fields while preserving the cultural significance of human-driven artistic expression. Originality/value This paper offers a novel exploration of the intersection between generative AI and copyright law, blending legal analysis with ethical and societal considerations. By examining real-world cases, emerging technologies and artistic perspectives, it provides a unique interdisciplinary approach to understanding the complexities of AI-generated art. The study’s originality lies in its integration of diverse viewpoints to propose a forward-looking framework for addressing legal, ethical and philosophical ambiguities. It contributes to ongoing debates by offering critical insights that are both theoretical and practical, relevant to stakeholders in law, technology and the creative industries.

The emergence of AI-generated artwork is challenging traditional notions of authorship, originality, and ownership. As a result, copyright norms worldwide are being reassessed. This study covers four major jurisdictions: the US, the European Union, the UK and China, through current theories and recent court cases. The data shows that the US and European Union adhere to strict rules that require human authorship, while the UK and China are exploring more flexible models. We also examine industry responses and issues of privacy and ethics in relation to the unlicensed use of copyrighted material for AI training. Based on the research on responsible AI and frameworks that prioritize transparency, fairness, and privacy, we suggest that a new legal category for machineassisted creativity should be established. This would recognize both human creators’ dignity and economic interests while emphasizing joint authorship. The analysis concludes with recommendations for the visual arts sector, including enforceable rules that require transparency around terms and conditions, a licensing system, ethical audits, and global governance of the sector through organizations such as WIPO.

The recent development of generative art, a typical category of artificial intelligence-generated content (AIGC), is essentially beneficial for social good, which can help amateurs to create artwork and improve experts’ efficiency. However, some artists are opposed to generative art technologies due to the copyright infringement and influence of the artists’ way of earning a living, which makes the artists protest against generative art technologies, causing a lose–lose situation. Adversarial attacks against generative model training are potential solutions to address this issue, while the lose–lose situation cannot be improved. To build a win–win situation, a feasible method is to incentivize the artists to actively contribute their artworks to generative model training without influencing their living or infringing copyright, such as data crowdsourcing, but traditional data crowdsourcing methods cannot well fit the generative art area. Therefore, this article builds a blockchain-based trading system for generative model training data collection and generated artwork circulation. Specifically, this article formulates a social welfare maximization problem based on the reverse auction and designs a corresponding incentive mechanism. The conducted theoretical analysis and numerical evaluation demonstrate the effectiveness of the proposed incentive mechanism toward a win–win situation for generative art model trainers and artists.

The last 3 years have resulted in machine learning (ML)-based image generators with the ability to output consistently higher quality images based on natural language prompts as inputs. As a result, many popular commercial “generative AI Art” products have entered the market, making generative AI an estimated $48B industry [125]. However, many professional artists have spoken up about the harms they have experienced due to the proliferation of large scale image generators trained on image/text pairs from the Internet. In this paper, we review some of these harms which include reputational damage, economic loss, plagiarism and copyright infringement. To guard against these issues while reaping the potential benefits of image generators, we provide recommendations such as regulation that forces organizations to disclose their training data, and tools that help artists prevent using their content as training data without their consent.

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Abstract:Recent studies highlight the potential benefits of generative AI tools in art education practices; however, art education scholars remain concerned about potential over-reliance, data bias, and risks of intellectual infringement. In this article, I analyze my two encounters with generative AI in art education from a posthumanist lens. This perspective enables us to see generative AI as an active participant in our agentic engagements in art education practices. By adopting a diffractive approach in understanding the role of AI in art education, I explore how art educators may coexist with generative AI and share accountability in the context of art education.

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Generative Artificial Intelligence (Gen AI) is rapidly reshaping the landscape of creative practice in the applied arts. While these tools accelerate ideation and support iterative prototyping, they also challenge traditional notions of authorship, authenticity and professional identity. This qualitative study explores how applied arts professionals integrate Gen AI into their workflows, what challenges they face, and what new skills and literacies they see as essential. Through purposive sampling, ten professionals, including designers, art directors, and filmmakers from diverse cultural contexts, were interviewed using semi-structured interviews. Thematic analysis identified two central themes: AI-driven workflow transformations and shifts in professional identity. Participants described Gen AI as a co-creator that enhances early conceptual work but also raised concerns around creative homogenization and ethical use of training data. These findings reinforce broader discussions in the literature about the dual role of AI as both a catalyst for innovation and a force that challenges creative diversity and cultural representation. The study highlights the need for a balanced approach to AI literacy in creative fields, one that integrates technical fluency with critical and ethical awareness. These insights provide a foundation for more nuanced, culturally sensitive, and ethically grounded approaches to AI adoption in the applied arts.

Generation of data, the fundamental and determining usage of data in AI, was fundamentally changed by deep learning generative models, which are now enabling AI systems to create their own usable data in innovative applications. With the evolution of these models(Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based architectures), there has been a breakthrough in systematic attempts to generate high-quality synthetic data and improve decision-making systems, providing numerous applications in various domains such as computer vision, natural language processing and digital art. Generative AI is allowing for realistic image synthesis, automated content creation, and adaptive problem-solving abilities by using neural networks to learn complex data distributions. Generative models have also been used to augment datasets on the fly in autonomous systems, helping improve the robustness and generality of AI models while reducing reliance on expensive human labels. While generative deep learning models have the potential to revolutionize various fields, they also present challenges, including concerns about training stability, computational complexity, and ethical implications. Continuing research addressing matters like mode collapse in GANs, interpretability problems, and biases in outputs must be undertaken to improve the performance and reliability of these models. Moreover, their rapid incorporation into applications like those in health care, finance, education, and entertainment also creates risk: of misinformation; issues with intellectual property (who owns the rights to the works produced); and data privacy (what happens if the basis of such a model were to be the unauthorized use of private data?) The need to address the above-mentioned challenges can be bellied by building more interpretable AI frameworks, enhanced adversarial training techniques and policy guidelines to ensure the ethical application of generative AI. Future deep learning generative models will increasingly incorporate hybrid approaches, requiring even less human oversight while executing a range of previously impossible tasks across different domains. Recent initiation of advances in efficient model architectures, self-supervised learning and federated training methodologies will further enhance the scalability and accessibility of generative AI. Given new advancements in research toward even more autonomous data generation, such models could be a key player in the next chapter of AI-related creativity, design, and automated- computer interaction.

We present DECORAIT; a decentralized registry through which content creators may assert their right to opt in or out of AI training and receive rewards for their contributions. Generative AI (GenAI) enables images to be synthesized using AI models trained on vast amounts of data scraped from public sources. Model and content creators who may wish to share their work openly without sanctioning its use for training are thus presented with a data governance challenge. Further, establishing the provenance of GenAI training data is important to creatives to ensure fair recognition and reward for their such use. We report a prototype of DECORAIT, which explores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data to determine training consent and reward creatives who contribute that data. DECORAIT combines distributed ledger technology (DLT) with visual fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance and Authenticity) standard to create a secure, open registry through which creatives may express consent and data ownership for GenAI.

Generative Artificial Intelligence (AI) advancements amplify concerns about the potential to appropriate Indigenous African cultural expressions such as songs, dances, and other forms of art. Generative AI systems autonomously generate diverse content, including music and art, but the supply chain of this new technology presents a complex challenge that may exacerbate cultural appropriation practices. Scholarship on the intersection of technology and Africa’s art and culture is animated by the theme of cultural appropriation and the need for protection against commercial exploitation. Likewise, there is a need for more research on how the unique nature of Indigenous African musical works increases their vulnerability to appropriation in the face of entrenched content digitalization practices and the cannibalization of these works as inputs to, and outputs from, generative AI systems. Therefore, this paper attempts to fill this literature gap by exploring the interplay of generative AI training datasets, Indigenous creative works, and the risk of cultural appropriation, with a particular focus on African music. The author argues that if unaddressed, generative AI systems have the potential to significantly erode the data and proprietary rights of various Indigenous communities in Africa, thereby undermining their ability to derive value from the protection of their intellectual property and sustainability of their cultural identity. Through a doctrinal analysis of extant and emerging policy, legal, and regulatory frameworks, this paper establishes the proprietary nature of Indigenous African music and its vulnerabilities in generative AI’s supply chain. The author makes recommendations that serve as a vital bridge between technology and cultural integrity, offering a pathway for responsible engagement with Indigenous cultural expressions and respectful utilization of Indigenous African musical works for generative AI systems to safeguard against misappropriation.

Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. These tools have potential benefits for artists, but they also have the potential to harm the art workforce and infringe upon artistic and intellectual property rights. Without explicit consent from artists, Generative AI creators scrape artists' digital work to train Generative AI models and produce art-like outputs at scale. These outputs are now being used to compete with human artists in the marketplace as well as being used by some artists in their generative processes to create art. We surveyed 459 artists to investigate the tension between artists' opinions on Generative AI art's potential utility and harm. This study surveys artists' opinions on the utility and threat of Generative AI art models, fair practices in the disclosure of artistic works in AI art training models, ownership and rights of AI art derivatives, and fair compensation. Results show that a majority of artists believe creators should disclose what art is being used in AI training, that AI outputs should not belong to model creators, and express concerns about AI's impact on the art workforce and who profits from their art. We hope the results of this work will further meaningful collaboration and alignment between the art community and Generative AI researchers and developers.

Structural information in images is crucial for aesthetic assessment, and it is widely recognized in the artistic field that imitating the structure of other works significantly infringes on creators’ rights. The advancement of diffusion models has led to AI-generated content imitating artists’ structural creations, yet effective detection methods are still lacking. In this paper, we define this phenomenon as "structural infringement" and propose a corresponding detection method. Additionally, we develop quantitative metrics and create manually annotated datasets for evaluation: the SIA dataset of synthesized data, and the SIR dataset of real data. Due to the current lack of datasets for structural infringement detection, we propose a new data synthesis strategy based on diffusion models and LLM, successfully training a structural infringement detection model. Experimental results show that our method can successfully detect structural infringements and achieve notable improvements on annotated test sets.

The integration of AI into the design industry is transforming image generation with novel creative opportunities, alongside new difficulties for designers. This research analyses the effects of emerging artificial intelligence image generation trends on user interface (UI) and user experience (UX) design and the growing role of the designer in an AI-enhanced creative workflow. The development of generative adversarial networks (GANs) for image-to-text programs such as DALL·E and Stable Diffusion has led to sophisticated and personalized virtual imagery. These technologies also allow for automated user interface (UI) element creation, content flexibility, and advanced design mockup construction, thereby improving the effectiveness of the design process. The application of AI in design comes with issues, including the violation of design integrity, ethical matter regarding bias and plagiarism, and dependence on automation. In this paper, we argue about the coexistence of human imagination and AI content under the premise that designers are able to use AI as an assistant, not as a replacement. Using a combination of literature review and case studies, we investigate the integration of artificial intelligence in imagery with the purpose of improving user experience (UX), accessibility, and personalization of user engagement.

Since the 21st century, image generation technologies have evolved beyond imitating human intuition to structuring visual emotions through artificial intelligence (AI). This study examines how the color sensibility embedded in Studio Ghibli’s animation posters is reproduced by generative AI. We conducted a quantitative color analysis of 20 representative posters directed by Hayao Miyazaki using the HSL (Hue, Saturation, Lightness) model, and compared the results with 16 AI-generated images produced under the same conditions. The analysis revealed that Ghibli posters consistently adopt a low-saturation color strategy centered on turquoise and ocher tones to evoke emotional immersion, while AI images tended to replicate similar chromatic structures. However, limitations emerged in the narrative flow and contextual depth of emotions, as the nuanced interaction between color and emotional progression was less sophisticated than that achieved by human designers’ intuition. User evaluations also indicated that while participants perceived strong visual similarity between the original and AI-generated images, they reported lower satisfaction regarding emotional diversity. Overall, the study demonstrates that AI-generated “Ghibli-style” images go beyond mere visual imitation by forming an independent emotional structure, yet remain constrained in capturing narrative depth. These findings provide a theoretical basis for understanding both the potential and limitations of algorithmic reproduction of color sensibility, and suggest practical implications for applying generative AI technologies in emotional design.

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AI image generators such as DALL-E 2 are deep learning models that enable users to generate digital images based on natural language text prompts. The impressive and often surprising results leave many people puzzled: is this art, and if so, who created the art: the human or the AI? These are not just theoretical questions; they have practical ethical and legal implications, for example when raising authorship and copyright issues. This essay offers two conceptual points of entrance that may help to understand what is going on here. First it briefly discusses the question whether this this art and who or what is the artist based on aesthetics, philosophy of art, and thinking about creativity and computing. Then it asks the question regarding human-technology relations. It shows that existing notions such as instrument, extension, and (quasi) other are insufficient to conceptualize the use of this technology, and proposes instead to understand what happens as processes and performances, in which artistic subjects, objects, and roles emerge. It is concluded that based on most standard criteria in aesthetics, AI image generation can in principle create art, and that the process can be seen as poietic performances involving humans and non-humans potentially leading to the emergence of new artistic (quasi)subjects and roles in the process.

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Image-generative models have gained popularity over the last years with their ability to create realistic artwork. Realizing complex artworks with specific creative ideas often requires iterative optimization of specialized prompts, but may still result in inadequate images. The inclusion of reference images and adapting modelspecific parameters can help in steering the model and fostering the creative intent of the user. But by providing text prompts, initial images, and adapting model parameters, users face a vast design space for creating images. To navigate through this space, we propose a visualization approach that combines an interactive Provenance Graph, parameter visualizations, and high-dimensional embeddings. Our approach helps pursue multiple parallel creation paths, makes workflows traceable and parameter changes transparent, and facilitates the reporting of image editing steps. In addition to prompt formulation, we focus on targeted generation by probing parameters, image compositions, and editing details. We integrate the generative process into existing image editing software, enabling users to compose artwork in collaboration with the model. The presented approach is evaluated in a user experiment ($\mathrm{n}=9$) for generating artwork. The results show that users with different levels of experience can create targeted artwork but use different strategies when working with the Provenance Graph.

AI painting has been widely used in recent years. However, the series of issues it raises in terms of copyright not only infringe upon the rights of relevant entities but also hinder the development of AI generation as a new technology. To this end, this paper focuses on the current state of copyright regulation and the corresponding mechanisms for AI painting. The research employs literature review, analysis, and case studies. It can be concluded that in the current environment where the law is relatively lagging, AI-generated content faces a series of complex challenges in determining originality, rights attribution, and infringement risks. To address these challenges, an integrated response strategy combining a "traceability mechanism for training data" and a "mandatory labeling mechanism" can be attempted to seek solutions.

In recent years, the rapid advancement of artificial intelligence (AI) has led to various creative practices in daily life, particularly in the field of art, which brings new legal challenges around copyright and AI-generated artwork. In previous marketing research, numerous studies have examined whether copyright can safeguard the rights of authors across different types of work, and have discussed the ownership of copyright in AI-generated artwork. However, a research gap remains between the connection of copyright law and AI-generated artwork. Moreover, under the prevailing legal framework, there is no specialized legislation to regulate the interaction between copyright law and AI-generated artwork, as well as the ownership of the copyright of the AI-generated products. Therefore, through a comparative analysis of cases about AI-generated artwork and related copyright law about AI products, this article will examine the legal nexus between copyright law and AI-generated artwork in order to fill the research gap around copyright law and AI-generated products.

: As the use of artificial intelligence technology, particularly deep learning models like the diffusion model, becomes more prevalent in the creation of art, concerns regarding the use of unauthorized work samples in their training have emerged. The lack of supervision and relevant laws has contributed to the problem. This study examines the potential infringement issues that may arise during the training of the diffusion model and explores the legality of using unauthorized samples for deep learning model training. While some scholars argue that copyright law only protects expression and not painting style, therefore, using unauthorized works in model training is not considered infringement, we propose a different viewpoint. By considering the essence of artificial intelligence from an information theory perspective, we highlight that it is still a deterministic algorithm and that data processing does not bring about an increase in information entropy without the input of additional information. Thus, the painting created by the diffusion model is essentially a mash-up of paintings in its training space, and as such, it is a copy or adaptation of the original work and should be licensed by the creator. We highlight the critical difference between human learning and AI "learning," emphasizing the need for effective protection and encouragement of human artistic innovation while embracing the wave of AI.

The development of Artificial Intelligence in a short period of time has had a crucial impact on the field of digital art, posing serious legal and ethical concerns as a part of the Copyright Law. This paper explores the AI-generated art and copyright, making a case of the issue of authorship, ownership, originality, and infringement in the modern visual culture. The study is multidisciplinary, incorporating legal analysis, comparative study and conceptual modeling to analyze the current copyright systems in major jurisdictions, such as the United States, European Union, United Kingdom and India. The results show that the existing copyright models are largely human centric and are poorly prepared to deal with the dynamics of AI-driven creativity. The confusion of the role of developers, users, and AI systems in the creative process makes attributing rights and responsibilities difficult. In addition, the fact that AI models are trained on copyrighted datasets poses infringement and fair use issues, and the international aspect of the digital platform exacerbates the inconsistency of regulations. The paper also examines how AI is affecting the wider visual culture, including how it is democratizing creativity, changing artistic activities, and thus redefining economic patterns within the creative sector. Nonetheless, it also highlights the issues of authenticity and artistic identity as well as ethical use of data. Through these reflections, this paper argues that adaptive law frameworks, models of hybrid authorship, and the globalization of copyright laws are necessary to handle the current situation in AI-generated art.

As the commercialization of artistic works increases, the scope of applied artworks under copyright law is expanding. Consequently, the concept and boundaries of design under design protection law have become ambiguous, highlighting the need to coordinate the protection and utilization of applied artworks by considering both copyright law and design protection law. The distinction between fine art and applied art is becoming increasingly blurred, making it more likely that the same work may be classified as either fine art or applied art depending on the context. With the advancement of artificial intelligence (AI) technology, generative AI is integrating with various domains such as design and branding, further exacerbating issues of overlapping protection. In modern legal systems, the dual protection of design and applied art is becoming more common, making legislative coordination a critical issue. Design protection law requires materiality and registration procedures, offering a relatively short protection period, whereas copyright law follows the principle of non-formality, granting automatic protection with a longer duration. As a result, an increasing number of creative works fall under both legal frameworks, leading to legal ambiguities. In particular, as digital design gains prominence, UI/UX, VR, and AR-based designs used in virtual spaces are at risk of falling into a protection gap. Although the amended design protection law now includes screen designs as a subject of protection, its coverage is limited to designs related to device operation and functionality, making it insufficient for protecting digital products within the metaverse. Therefore, there is a need to relax the materiality requirement in design protection law and expand the protection scope of applied art designs in coordination with copyright law. Establishing clear legal standards is essential to reducing legal uncertainty and resolving boundary issues between applied artwork and industrial design protection. While dual protection exists, it is necessary to expand copyright-based protection. The protection of applied art should be approached differently depending on whether it is mass-produced. For industrial products that are manufactured in large quantities, prioritizing the free use of design is crucial, necessitating restrictions on copyright protection. Conversely, in cases where applied art is difficult to mass-produce, copyright protection should be strengthened. For applied artworks, many creators prefer copyright law over design protection law due to its complex procedures and high costs. Hence, expanding copyright-based protection and implementing a system where works are automatically protected without registration procedures is necessary. Digital designs, such as virtual goods, have a strong commercial nature but lack materiality, making them difficult to protect under design law. As such, they should be safeguarded under copyright law. Additionally, the design disclosure certification system should be extended to applied artworks, enabling the preemptive proof of rights under copyright law. Furthermore, incorporating the “Temporary Standards” system from the Act on the Promotion of Virtual Convergence Industry into the protection of applied art in virtual environments could provide a flexible regulatory framework. By applying ex-post regulation rather than preemptive regulation, it would ensure a legal structure that supports industrial growth without hindering innovation.

With the development of AI technology, particularly the emergence of models such as Stable Diffusion, AI-generated artworks have garnered widespread attention. The "creative" capabilities of AI rely on neural network technology, and the legality of its training data has become a focal point of controversy. Simultaneously, the issue of copyright ownership for AI-generated works urgently requires clarification. Furthermore, the impact of AI technology on traditional artistic creation, such as lowering the barriers to creation and threatening employment opportunities for practitioners, has sparked dissatisfaction among artists. This paper employs a combination of literature analysis and case studies to explore the controversies surrounding AI-generated technology in artistic creation, analyzing issues related to the legality of training data sources, copyright attribution, and its disruptive effects on traditional artistic practices. The study proposes strengthening the protection of original works, increasing transparency in AI training data sources, and establishing sound commercial use regulations for AI. These measures aim to balance public interests and individual rights, fostering harmonious development between AI technology and artistic creation.

This essay introduces Buccaneer Piracy, a novel form of AI-driven cinematic replication that bypasses traditional content theft by reconstructing entire films using publicly available information and generative AI tools. Drawing parallels to 17th-century buccaneers, the concept reflects a modern form of digital piracy that exploits gaps in copyright law and technological ethics. Through a hypothetical case involving a Snow White remake, the paper outlines the mechanics of Buccaneer Piracy including AI-generated scripts, deepfake casting, and synthesized video production and contrasts it with conventional piracy. It examines the legal ambiguities, ethical dilemmas, and potential economic and creative impacts on the film industry. The study concludes with a call for legislative reform, technological safeguards, and public awareness to mitigate the risks posed by this emerging threat, emphasizing the need for a balanced approach in an increasingly AI-driven entertainment landscape.

This article explores the upcoming issues and legal challenges in copyright law brought by AI-generated artworks. As AI technologies improve, the creation of art by AI has raised problems regarding authorship, originality, and the application of existing copyright frameworks. By analyzing cases happened in different region about copyright in AI-generated art, it is discovered that different attitudes toward AI-generated artworks under current copyright framework. While the United States show a relatively conservative stance, insisting that the role of author must be human, other countries such as Can dada and China began to admit the authorship of AI, accepting AI as a way to achieve creativity and originality. Based on the existing situation, the article provided possible solutions, aiming to protect copyright of creative artworks generated by AI and accept AI as artistic tool that could increase efficiency and creativity.

With the fast development of artificial intelligence, the creative industries were changing, as machines can produce highly developed works of art that question the conventional concept of creativity, authorship, and property rights. With the rising popularity of AI-generated art, the intellectual property (IP) law and policy concerning AI-generated art have become a pressing legal and policy concern. This paper explores the intellectual basis of AI-created pieces of art, emphasizing the role of human elements and machine-directed algorithms leading to the overall artistic work. It then makes an assessment of the existing intellectual property rights systems such as copyright, patents, trademarks and moral rights to see how they apply to AI-assisted and fully autonomous works. The legal issues in the center are examined, and specific focus is made on the questions of authorship attribution, allocating ownership and copyright deserving, and liability of developers, users and AI systems in cases of infringement. Comparative analysis of approaches in the world has shown that there is a great variance that exists among the jurisdictions. The United States tends to use a model of authorship that is more human-focused, restricting the copyright rights of the work that does not imply significant human contribution. The European Union gives the priority to human intellectual creative but is seeking alternative regulation solutions. Asian jurisdictions including Japan, China and India are actively evolving guidelines to find a balance between innovation and legal certainty with each having its own policy orientation. International organizations specifically WIPO are trying to make global standards harmonize despite different national interpretations.

While artificial intelligence (AI) stands to transform artistic practice and creative industries, little has been theorized about who owns AI for creative work. Lawsuits brought against AI companies such as OpenAI and Meta under copyright law invite novel reconsideration of the value of creative work. This paper synthesizes across copyright, hybrid practice, and cooperative governance to work toward collective ownership and decision-making. This work adds to research in arts entrepreneurship because copyright and shared value is so vital to the livelihood of working artists, including writers, filmmakers, and others in the creative industries. Sarah Silverman’s lawsuit against OpenAI is used as the main case study. The conceptual framework of material and machine, one and many, offers a lens onto value creation and shared ownership of AI. The framework includes a reinterpretation of the fourth factor of fair use under U.S. copyright law to refocus on the doctrinal language of value. AI uses the entirety of creative work in a way that is overlooked because of the small scale of one whole work relative to the overall size of the AI model. Yet a theory of value for creative work gives it dignity in its smallness, the way that one vote still has dignity in a national election of millions. As we navigate these frontiers of AI, experimental models pioneered by artists may be instructive far outside the arts.

In this article, we intend to discuss the growing influence of artificial intelligence (AI) in the fields of art. The phenomenon, which has long been familiar in the visual arts sector, is now also gaining relevance in the music sector. The impact of AI on music, particularly on the more avant-garde and experimental sub-genres, is generating debates ranging from copyright, authenticity and, above all, creativity. The specific case discussed in this one is the album Diotima by New York band Krallice, released in 2011, and its ‘counterpart’, Coditany of Timeness, generated by Dadabots, a collective experimenting with AI, created using a neural network trained on Diotima. Released at NeurIPS 2017, Coditany of Timeness exemplifies how AI can reinterpret musical styles, enabling a reflection on ‘machinic’ creativity. Both albums were subjected to analysis with regard to song structure, melody and harmonic language. The creative dimension behind the project was deepened through a textual analysis of articles and online interviews, a total of 37 documents. This set of results was then discussed with members of the Krallice through a semi-structured interview. The intention is to explore how AI interacts with the human creative process, asking questions about how the machine interprets these processes, and how these results are perceived by the artists themselves.

The implementation of generative AI tools has not only reshaped the contemporary art ecosystem through AI-generated artworks, but also triggered ethical dilemmas such as the erosion of subjectivity, copyright ambiguities, and aesthetic alienation. These challenges have compelled a paradigm shift in traditional art ontology. This study examines the reconstruction of art ontology in human-machine collaborative contexts, using ethical dilemmas as a starting point and combining commercial applications with avant-garde practice cases. It reveals that traditional creator identity definitions and copyright frameworks struggle to adapt to new creative models, while technological monopolies exacerbate aesthetic homogenization and fairness crises. Art ontology must transition from anthropocentric perspectives to a "human-machine-data" collaborative system framework, with value evaluation standards incorporating both technological innovation and cultural compatibility. Future efforts should focus on establishing interdisciplinary dynamic ethical norms, advancing copyright law reforms and tiered rights confirmation mechanisms, while balancing technological iteration with artistic integrity through humanistic values.

Artificial Intelligence (AI) has ushered in a revolutionary change that mixes creative abilities between human composers and computer-powered intelligence during musical composition. The investigation examines the musical application of collaborative AI which exists as an aid to composers by suggesting ideas and creating motifs alongside enhancing musical arrangements. OpenAI’s MuseNet alongside Google’s MusicLM brought about new generative model technologies which enable musicians to have real-time access to adaptive tools that interpret as well as transform musical concepts. Based on secondary research and case studies, the article examines human composer-AI system partnerships to explain how their combined work restructures artistic authorship and creative methods. The paper uses today's artists with AI support and collaborative works between different fields to demonstrate the partnership's core dynamics. The discussion explores two main elements about AI music production which are human involvement versus programming automation alongside understanding emotional integrity in synthetic musical compositions together with co-creative copyrights regulations. This research evaluates how partnership between humans and AI components transforms musical education along with the process of composition for those without a musical background while testing established artistic boundaries of genre classification and original content production. This research project depicts AI as an amplification force that generates human creativity rather than being considered disruptive by showing how intelligent feedback systems work together with human agents. Co-creation behavior in this hybrid method motivates a fresh depiction of musical expression which sparks explorations about art creation and authorship roles and identity function in the future.

Background. The 2025 Polish viral song “Tak kocha się tylko w filmach – Ejaje 1981” was wholly generated by artificial intelligence (AI) to emulate a fictional 1981 pop-disco hit, got a few million views in streaming media in May/June 2025 making it an ideal case for studying AI’s impact on music creativity and reception. Method. Using an interdisciplinary approach, we conducted (i) a musicological analysis of Ejaje 1981, (ii) legal and phonographic market perspective (iii) a sociological analysis of ~1200 social-media comments and sharing patterns across platforms. (iv) foresight, is this viral event an outlier or indicative of a larger shift in music culture? Results. The AI track blends disco-pop and retro timbres convincingly, yet copies protected lyrics and creates melody. Virality was driven by TikTok remix culture, cross- generational nostalgia, and a “false-memory” effect wherein older listeners believed they had heard the song decades earlier. Discussion. Findings align with prior AI-music precedents (i.e. Suno-GEMA dis- pute) indicating that generative AI is reshaping perceptions of authenticity, authorship, and market entry barriers. Hit single cessation was previously associated with a “divine spark”, now it was engineered. We show specificity of the Polish phonographic market and local tastes as an example of probably the first fully AI-generated single hit here. Conclusions. Ejaje 1981 exemplifies a broader cultural shift: AI now influences how music is created, circulated, and remembered.

The rapid emergence of AI-driven music technologies is transforming the creative economy worldwide, with Indian musicians and industry stakeholders facing new challenges and opportunities. This paper examines how generative AI is reshaping employment in India’s music sector, integrating global trends with India-specific data and case examples. We begin by situating AI adoption within India’s burgeoning creative industries (now contributing roughly 8% of the country’s employment). A literature review highlights global analyses of AI’s impact on music and India-focused studies on creative economies. We then present case studies: Indian startups and artists (e.g. Beatoven.ai, NaadSadhana, independent musician Jake Joss, and legal cases like singer Arijit Singh’s AI-voice ruling) that illustrate how technology is being adopted or contested. Survey results and reports (e.g. Asian Development Bank and Stanford research) suggest millions work in creative jobs and hint at declining employment in AI-exposed roles, though Indian employers remain largely optimistic about new AI jobs. We discuss how AI threatens some traditional roles (composers for jingles, session players, voice artists) while creating new ones (AI trainers, prompt engineers, creative collaborators). Musicians face pressures to adapt skills or risk displacement, but some see AI as a creative aid. Finally, we examine ethical and socio-cultural implications in India – from copyright and personality-rights challenges to concerns over cultural authenticity and digital equity. In conclusion, AI is neither a job-killer nor a panacea: its long-term impact will depend on regulation, business models (e.g. royalty-sharing AI music models), and how artists embrace or resist these tools.

The integration of neural networks into contemporary painting represents a profound transformation in artistic production, aesthetic theory, and creative authorship. This study examines how artificial intelligence (AI), particularly deep learning architectures such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, functions within modern painting practices. Employing an interdisciplinary IMRAD framework, the research combines technical analysis of neural architectures, case studies of AI-generated artworks, and theoretical evaluation of creativity, authorship, and aesthetic agency. The findings indicate that neural networks enable stylistic emulation, generative image synthesis, semantic text-to-image translation, and large-scale visual recombination, thereby expanding both the visual and conceptual boundaries of painting. However, their outputs reflect procedural and statistical creativity rather than conscious intentionality. The study further demonstrates that AI reshapes artistic workflows by shifting emphasis from manual execution toward prompt engineering, system configuration, dataset curation, and algorithmic mediation. While AI-generated paintings have achieved institutional validation and market recognition, they simultaneously raise significant ethical and legal concerns regarding copyright ownership, dataset consent, artistic labor displacement, and cultural appropriation. Ultimately, neural networks function not as autonomous artists but as transformative creative media that redefine collaboration between human imagination and machine computation in contemporary art.

With a focus on producing a new AI-based filmmaking platform, this study explores the revolutionary impact of AI on the film industry. Because of how movies are envisioned, made, and seen, it is drastically shifting due to AI. An in-depth analysis of the emergence and applications of AI in film is provided in this article, with particular attention to how these technologies are incorporated into the screenplay writing, pre-production, cinematography, editing, and visual effects phases of the filmmaking process. It focuses on how blockchain technology is revolutionizing the management of intellectual property and film distribution, how Natural Language Processing (NLP) is automating the drafting of scripts, and how generative AI is producing novel visual styles and narrative structures. AI transforms filmmaking in technical and artistic aspects by increasing production, creating new avenues for creativity, and providing distinctive visual forms. Aside from these important ethical issues, the article also discusses employment displacement, copyright infringement, authorship, creative purpose, and the possibility that AI will reinforce prejudices or preconceptions. A focus on new rules for the moral and responsible use of AI that balance the advantages of automation with the need for human creativity and oversight is placed on the legal advancements governing AI in the film industry. The research also examines how audiences respond to and understand these novel storytelling techniques by contrasting the aesthetics of traditionally made movies with those produced by AI. AI films may need help to retain emotional depth and narrative coherence, even though they frequently use cutting-edge visual effects and interactive storytelling. The study includes case studies and practical scenarios, such as the AI-generated film Sunspring, to interpret the real-world application of AI in filmmaking. Besides, the article extends AI’s role in audio and sound design, highlighting improvements in automated dubbing, sound effects synthesis, and music composition. Our research sheds light on how AI will affect filmmaking in the future, which adds to the larger conversation on the impact of emerging technologies on the creative industries. As the film business develops, AI presents important issues that need to be carefully handled, in addition to the tremendous prospects it gives for innovation and democratization.

There are severe issues that bring the impact of AI enabled systems into sharp focus for the cultural heritage sector, especially when these platforms are open, extremely tempting, and now accessible for everyone who may wish to create art or write a novel at the keyboard. Creating the verbal cue and waiting for the sublimity of every desire is mesmerizing, enthralling, even addicting but the impact on human creativity somehow feels like an accident waiting to happen. This profuse human/machine creativity resulting in AI artifacts sets up enormous challenges for all players in the creative industries, from the struggling artist or designer; to the public-funded institutions such as museums who will be wandering the labyrinth of copyright policies, trying to inscribe the metadata of the provenance of AI production. The paper will discuss Chat GPT and present AI imagery as doppelgängers that seemingly emerge out of an uncanny valley. The process is almost immediate with DALL-E2, Midjourney, and Stable Diffusion all providing instant results at the drop of a prompt. With a series of case studies, we will discuss the challenging copyright and provenance issues that are taking place at a dizzying pace as a sector that is striving to come to terms with the highly problematic issues of authenticity and ownership.

Artificial intelligence (AI) deployed for customer relationship management (CRM), digital rights management (DRM), content recommendation, and content generation challenge longstanding truths about listening to and making music. CRM uses music to surveil audiences, removes decision-making responsibilities from consumers, and alters relationships among listeners, artists, and music. DRM overprotects copyrighted content by subverting Fair Use Doctrine and privatizing the Public Domain thereby restricting human creativity. Generative AI, often trained on music misappropriated by developers, renders novel music that seemingly represents neither the artistry present in the training data nor the handiwork of the AI’s user. AI music, as such, appears to be produced through AI cognition, resulting in what some have called “machine folk” and contributing to a “culture in code.” A philosophical analysis of these relationships is required to fully understand how AI impacts music, artists, and audiences. Using metasynthesis and grounded theory, this study considers physical reductionism, metaphysical nihilism, existentialism, and modernity to describe the quiddity of AI’s role in the music ecosystem. Concluding thoughts call researchers and educators to act on philosophical and ethical discussions of AI and promote continued research, public education, and democratic/laymen intervention to ensure ethical outcomes in the AI music space.

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The emergence of AI-generated art has sparked a debate on the ethical considerations and social impacts of this new form of creativity. This paper explores the current approaches to AI (Artificial Intelligence) art generators, including the ongoing debate on AI arts creativity and the issues of copyright, ownership, and fair use. Meanwhile, it systematically examines the standard procedures of AI art generators, including machine learning models such as GANs (Generative Adversarial Networks) and text-to-image models. Moreover, the psychological views of art are explored, highlighting the importance of novelty and unexpectedness in determining the relevance of stimuli. The paper presents arguments in favor of and against AI-generated art as a form of creativity, considering factors such as the expression of emotions, the uniqueness of the artwork, and the copyrightability of AI-generated content. The discussion delves into the fair use of copyrighted images in AI-generated art and evaluates the four factors of fair use. The differences in copyright laws internationally are also examined, with some countries recognizing ownership of AI-generated content by programmers. Finally, the paper concludes that AI-generated artwork has the potential to be a positive force in the art world while acknowledging the need for further research and discussion on the legal and ethical implications of AI-generated art.

The rapid development of AI-generated art that mimics the signature styles of human artists, exemplified by the ghiblification phenomenon, poses a crucial problem for the copyright protection of artistic style, which is traditionally unprotected. This research aims to critically analyze this legal challenge, conduct a comparative study of the copyright regulatory frameworks in Indonesia, the United States, the European Union, and Japan concerning AI art and the issue of artistic style, and formulate policy recommendations for Indonesia. Using a normative juridical research method through statute, conceptual, and comparative approaches, this study examines the legislation, doctrine, and practices in these four jurisdictions. The results show that ghiblification confirms the vulnerability of artistic style; although style as an idea is not protected, the replication of specific expressions by AI can still potentially constitute copyright infringement. The comparative analysis reveals significant variations in approach: the United States strictly requires human authorship, Japan offers flexibility for using data for AI training with a proviso, and the European Union seeks a balance through a TDM exception, while Indonesia still faces a specific regulatory vacuum. Nevertheless, a global consensus exists on the importance of human creative contribution for the recognition of copyright. It is concluded that the existing copyright legal framework, particularly in Indonesia, is inadequate to respond to the disruption of AI-generated art, thus requiring urgent juridical adaptation. This study recommends legal reform in Indonesia, including the clarification of the status of AI-generated art, the consideration of licensing models, and the strengthening of moral rights aspects in order to balance technological innovation with fair artistic protection.

This paper explores the developing relationship between artificial intelligence (AI) and creativity, focusing on how generative tools such as Midjourney, ChatGPT, and Stable Diffusion challenge traditional understandings of artistic production, authorship, and labor. Drawing on the relational-materialist framework developed by Celis Bueno, Chow, and Popowicz (2024) and grounded in Lievrouw's (2014) diagram of mediation, this paper examines the dynamic interactions between technological artifacts, creative practices, and social arrangements. Central to this analysis are the concepts of creative labor, automation, and distributed agency, which help to demonstrate how AI tools function not only as instruments but also as active agents in the creative process. While AI is often celebrated for democratizing creativity and enabling new forms of artistic expression, the paper highlights the ethical and economic concerns surrounding the commodification and automation of creative labor. The concept of “knowledge extractivism” (Pasquinelli and Joler 2021) is used to describe how AI systems are trained on vast datasets of human-generated content, often without consent or compensation, raising critical questions about ownership, distribution of value, and exploitation. In addition, the notion of “mean images” (Steyerl 2023) highlights how AI-generated outputs often reflect statistical averages rather than true innovation. By situating AI creativity within broader systems of data capitalism and epistemic colonialism, the paper challenges narratives of co-creation and calls for a more critical understanding of agency, authorship, and power in AI-driven cultural production. Ultimately, it argues that while AI can augment human creativity, it is not inherently creative. Instead, it functions by remixing and repurposing human labor, necessitating new regulatory frameworks and ethical considerations to ensure a fairer creative future.

The accelerated development of AI-created sculptural works has set new standards of creativity, authors, and expression of material, but it also delivers some serious ethical issues, questionable by conventional artistic and cultural paradigms. As the roles of generative models, mesh networks in 3D and computational fabrication tools continue to be integrated into the sculptural ideation and production processes, questions of authenticity of the sculptural intent, the validity of the hybrid human machine authorship arises as well as the risk of losing the craft-based knowledge systems. The transparency of AI algorithms also causes ethical concerns that the algorithm might include some sort of hidden bias that form the formal aesthetics, cultural themes, or symbolic forms in a manner that unintentionally misrepresents or steals heritage traditions. Additionally, culturally significant, or proprietary art, is frequently presented as a part of a training dataset, and understandings of the intellectual property rights, permission, and ethical obligations of the creators and institutions using such systems may be disputed. The second ethical factor is that the AI-generated sculptures can be commodified and scaled at a mass level, thereby causing disruptions in the socio-economic ecosystems of sculptors, teachers, designers, and local craft communities. At the same time, the standardization of the algorithmic optimization poses a danger of homogenization of art diversity, thus reducing pluralism of expression of sculptures between cultures. Additional environmental aspects such as the energy requirements of training models as well as the material disposal of rapid prototyping makes the issue of AI-driven sculptural practices even more consequential. This abstract shows the necessity to create transparent, accountable, and culturally respectful AI models that would be able to protect human creativity, the integrity of the arts, and fair co-existence between technological innovation and ancient sculptural arts.

The information presented in this paper explores the changing ethical issues that come with AI-generated artworks, and how algorithms, datasets, and the behavior of model-layers, cultural sensitivity, and the governance structures influence artistic results. The research evaluates various aspects of ethical risk, such as the spread of bias, stylistic theft and misrepresentation of culture, as well as manipulation of identity. Based on a multi-layered analytical framework upheld by graphs, tables, and case studies, the study points out that the main ethical vulnerabilities are due to uneven datasets, obscure semantic-layer reasoning, and inadequate protection of culturally sensitive information. To illustrate how the failures of ethics are put into practice, the paper introduces five real-world case studies that include unauthorized style mimicry, distortion of Indigenous symbols, the bias of a demographic portrait, exploitation of community heritage datasets, and manipulation of identity by a deepfake to prove how they may fail in practice. The results show that governance interventions, including consent-driven datasets, fairness auditing, cultural-protection mechanisms, and identity safeguards, can greatly mitigate the harmful output, which is achieved through their regular use. The paper ends with an overview of the future directions that focus on contextual cultural intelligence, adaptive governance, semantic interpretability and community oriented data rights. The general aim is to influence the creation of morally accountable generative AI applications, which are cognizant of creative authority, culture, and social trust.

Artificial Intelligence (AI) is rapidly transforming the field of visual design by introducing new tools and techniques that enhance creativity, efficiency, and scalability. AI-driven systems such as generative design models, computer vision algorithms, and automated layout tools are enabling designers to produce high-quality visual content with reduced time and effort. These technologies assist in tasks including image generation, color palette selection, layout optimization, and user experience personalization. AI-powered design platforms are increasingly used in industries such as advertising, marketing, entertainment, web development, and product design, allowing organizations to streamline workflows and generate visually appealing outputs. Despite these benefits, the integration of AI in visual design raises significant ethical concerns that require careful consideration. Issues related to intellectual property rights, originality, data privacy, algorithmic bias, and the potential displacement of human designers have sparked ongoing debates. AI models are often trained on large datasets containing copyrighted artwork, leading to questions about ownership and fair use. Furthermore, biased training data may result in designs that unintentionally reinforce stereotypes or exclude certain cultural perspectives. The increasing automation of creative processes also challenges traditional notions of authorship and creativity, prompting discussions about the role of human designers in AI-assisted environments. This paper explores both the opportunities and ethical implications of AI in visual design. It examines how AI technologies can augment human creativity while highlighting the importance of responsible AI development, transparency, and ethical guidelines. By analyzing current applications, challenges, and future directions, the study aims to provide insights into achieving a balanced integration of AI that supports innovation while safeguarding ethical and professional standards in visual design.

The fast development of the generative artificial intelligence has considerably altered the modern artistic operations, posing the essential concerns about the matter of originality, authorship, and artistic worth of the AI-generated products. Although AI systems can create visually attractive and stylistically varied results, it is a more pressing problem how to judge whether these were original creations or just recombinations of acquired information. This analysis suggests a holistic analysis of originality in AI modern art by joining the computational evaluation with human analysis. The study constructs originality as a multidimensional phenomenon that covers novelty, non-traditionality, intention to create something new, and relevance to its contexts in terms of cultural and historical reference space. The proposed framework is based on the theories of computational creativity and human-AI co-creativity, but it also considers the shared authorship models where originality is created through the interaction between artists, datasets, algorithms, and curatorial choices. The originality assessment model based on AI is presented and is a combination of visual, semantic, stylistic, and contextual feature extraction with embedding-based similarity and divergence analysis. The quantitative measure of originality in terms of novelty scores, stylistic distance measures and entropy based diversity measures are used to represent the structural and statistical aspects of originality. These calculation tests are then complemented by qualitative tests such as the art experts, curators and audience perception studies in order to cover the subjective and interpretive aspects which most automated programs fail to cover. A comparative study of AI-based evaluation and traditional originality assessment methods shows the advantages and the constraints of the former.

The development of AI in recent years has led to the emergence of the problem of whether AI-generated content is copyrightable. A sound & widely accepted standard for this problem is still pending. This paper adopts the perspective of Idea-Expression Dichotomy to explain why prompts provided to AI should be regarded as ideas rather than expression. It further discussed that the substantial human modification is the fundamental factor for AIGC to obtain copyrightability. Based on which, this paper proposes that the human use of corresponding artistic language serves as the basis for determining whether Expression is realized in works based on AIGC. This criterion regards the human substantial modification as the key factor for judging the intellectual contribution reflects in the works, which synchronized with the legislative purpose of the Copyright law to encourage creation. Meanwhile, this paper argues for how to apply technical approaches in the judgement, so as to reduce the judicial burden.

The rapid development of artificial intelligence technology is capable of interpreting and creating visual artwork based on human input through a platform. Although AI offers efficiency in creating works, the similarity to existing visual styles sparks debate about originality and ethics. This issue gained prominence with the emergence of AI generated Studio Ghibli-style digital portraits on social media. The main issue discussed in this study is how AI-generated images differ visually and technically from Studio Ghibli's original works. Previous studies focused on copyright and data privacy, research comparing visual is still limited. This research aims to fill this gap by conducting a visual comparative analysis between AI-generated works and Studio Ghibli's animation style using Feldman's theory. This comparison clearly shows how AI-generated visuals compare to human-made ones based on visual and technical analysis.The findings show that AI can copy surface features well, but it cannot achieve accurate anatomy, detail, visual consistency, or emotional depth correctly. The study concludes that AI still can't fully copy the emotional and visual depth that human artists can create. The finding emphasizes the uniqueness of original artwork and is expected to provide significant insights for the creative industry, opening up space for ethical discussion and enhancing visual literacy among designers, academics, and the public.

Differentiating AI-generated, real, or imitated artworks is becoming a tedious and computationally challenging problem in digital art analysis. AI-generated art has become nearly indistinguishable from human-made works, posing a significant threat to copyrighted content. This content is appearing on online platforms, at exhibitions, and in commercial galleries, thereby escalating the risk of copyright infringement. This sudden increase in generative images raises concerns like authenticity, intellectual property, and the preservation of cultural heritage. Without an automated, comprehensible system to determine whether an artwork has been AI-generated, authentic (real), or imitated, artists are prone to the reduction of their unique works. Institutions also struggle to curate and safeguard authentic pieces. As the variety of generative models continues to grow, it becomes a cultural necessity to build a robust, efficient, and transparent framework for determining whether a piece of art or an artist is involved in potential copyright infringement. To address these challenges, we introduce ArtUnmasked, a practical and interpretable framework capable of (i) efficiently distinguishing AI-generated artworks from real ones using a lightweight Spectral Artifact Identification (SPAI), (ii) a TagMatch-based artist filtering module for stylistic attribution, and (iii) a DINOv3–CLIP similarity module with patch-level correspondence that leverages the one-shot generalization ability of modern vision transformers to determine whether an artwork is authentic or imitated. We also created a custom dataset of ∼24K imitated artworks to complement our evaluation and support future research. The complete implementation is available in our GitHub repository.

Arguably, Artificial Intelligence is the most impactful factor in the life of the common man. It has touched almost every aspect of life. However, its use in the field of art and literature has raised several eyebrows amongst the critics and litterateurs alike. The main points of concern are related to ethics, plagiarism and the growth of dishonesty. With the help of AI, a sudden spurt of artists, authors and other literary people has sprung forth on the literary horizon. Now, anyone can become a writer, poet, artist or so, within no time. There is no need for talent and calibre. As a consequence, a large number of such fake talents in the field of art and literature are producing their AI-inspired works without making any substantial effort. Not only this, these pseudo-artists and writers are also making money. In the field of cinema, AI-generated scripts and music have become the order of the day. Another frightening aspect is the growth of vandalism. Some unscrupulous people have even tried to create caricatures and funny variants of many famous paintings by renowned painters. The disfiguration of the world-famous painting of Mona Lisa by Leonardo da Vinci is a classic example. In short, the world of literature and arts is likely to experience a cataclysmic change in the near future if the proper checks and controls are not exercised by the lawmakers in this regard. This paper explores various dimensions of the effects of AI in the regime of art and literature and tries to find the probable answers.

Character design aims to convey a strong visual and personality identity, as exemplified by successful industry characters such as Spider-Man. The strong identity of Spider-Man has inspired extensive responses in the form of digital art design, including the boundless realm of AI-generated art. The phenomenon of AI’s openness on social media raises questions about the authenticity of designs produced by AI, particularly works featuring Spider-Man as a popular design. Ultimately, within the scope of Visual Communication Design studies, comparing original versus AI-generated Spider-Man character designs through Manga Matrix analysis becomes essential to understand which elements are retained and which distinguish the authenticity of the Intellectual Property’s visual characteristics. This research employs a descriptive qualitative approach using Manga Matrix analysis and focus group discussions (FGD). The results show that similarities in several Manga Matrix elements within AI works indicate that the character still refers to the original Intellectual Property. Conversely, the Personality aspect tends to disappear when the Intellectual Property’s visual elements are modified, resulting in differences from the original visual elements. This research may contribute design recommendations for designers by indicating that Personality aspects can change if the visual elements that compose them are modified by AI