Abstract

Bu çalışma, yapay zekâ (YZ) ile üretilen sanat eserlerinin orijinallik, yaratıcılık ve telif hakkı bağlamındaki statüsünü incelemeyi amaçlamaktadır. Araştırma kapsamında, sanat felsefesi ve dijital medya kuramları ışığında orijinallik kavramı yeniden değerlendirilmiş; ayrıca üretici yapay zekâ (generative AI) sistemleriyle oluşturulan görseller içerik analizi yöntemiyle incelenmiştir. Sanatçılar ve tasarımcılarla yapılan derinlemesine görüşmeler, YZ ile insan yaratımı arasındaki estetik ve etik sınırların giderek bulanıklaştığını göstermektedir. Bulgular, YZ'nin geçmiş verilerden üretim yapması nedeniyle özgünlük iddiasının tartışmalı olduğunu; ancak yeni ifade biçimleri yaratarak sanatsal yorumlamaya açık alanlar sunduğunu ortaya koymaktadır. Sonuç olarak, YZ sanatında orijinallik kavramının mutlak değil, bağlamsal ve yoruma açık bir nitelik taşıdığı savunulmakta ve sanat teorisinin bu yeni yaratım biçimlerine uyum sağlayacak şekilde yeniden düşünülmesi önerilmektedir.

Abstract

The art production based on artificial intelligence is a turning point; it fundamentally challenges the essence of an artistic creation process and reinterprets what can be understood as originality. It uses deep learning models that are trained on a couple of large datasets to create new visual content (including Generative Adversarial Networks and diffusion). The major ethical and academic question arises from the fact that these systems have been trained on innumerable copies of works by humans, with their outputs being extracted directly from those pieces.

Originality also presents a complex issue when it comes to AI art. The system can create outputs that are blatantly derivative or imitative, simply mimicking the statistical makeup of prior works. Alternatively, it can create novel and random content that are beyond the artistic capacity of even an average human artist. The actual fine line is not between these two extremes, but resides within the process.Congratulations for reading this article! Want to support my work? AI narrows down creativity to extracting patterns from data and re-synthesizing them in novel combinations. As a result, the human definition of uniqueness (intention/ emotion/expression) is eclipsed by data scale and diversity coupled with prompt engineering from users.

The central ethical discussion relates to consent and authenticity. Using artist works (most without permission or payment) for training data: is that intellectual property infringement? Is this enough to compel protection for the new work under "transformative" use? The human creative process that precedes the final work is what copyright law revolves around, and AI art hides this causal link. As an interdisciplinary research area, a field of academic art history must address this gray area in ways that merely updating legal frameworks will not cover; likewise "creativity," "originality," and “artistic authority” need to be rethought for the digital age. After all is said and done, its place as a tool in art history should be respected but requires comprehensive guidelines for ethical production that acknowledge the heritage of human culture it feeds off.

Structured Abstract: Introduction & Purpose of Study — The use of artificial intelligence (AI) in the production and creation of art has fueled heated discussions regarding originality, authorship, and creativity. AI-driven art platforms, especially powered via GANs (Generative adversarial networks) or diffusion models with large-scale visual datasets are proficient at generating images that look like an artistic style but also create completely original visuals. This twofold ability begs the most important question in contemporary digital art discourse: Where does original creation stop, and where does imitation begin with AI-generated artworks?

This study mainly aims to explore and understand the conceptual as well practical boundaries between originality and copying when it comes to AI-generated art. The research is then attempting to answer the following questions: (1) In the case of AI-generated art, what does originality mean and how do we evaluate it? (2) To what extent are AI-generated artworks derivative or transformative as opposed to exact reproductions of preexisting works? (3) What ethical, legal and aesthetic problems arise relating to the growing use of AI as a creative agent? In summary, the rapid growth of AI-mediated art production raises important questions about the meaning behind ownership and creative intent that lack a coherent theoretical framework to help define originality.

Conceptual and Theoretical Framework: The rationale behind this study is based on the interdisciplinary literature in aesthetics, art theory studies, creativity research and artificial intelligence (AI). Contrast this with postmodern notions of appropriations, remixes & intertextuality to the classical theories which situated conceptions of originality in art within a broader context shaped by modernist ideas rooted around the individual genius and intentional authorship. Walter Benjamin and Roland Barthes are ever-present in the literature on reproducibility of art, with their thoughts forever related to algorithmic creation by way of death-of-the-author.

In AI, creativity is commonly described as the area of novelty produced in computations driven more by probabilistic pattern recognition and not intentional awareness. Much of the prior work points out that AI systems base their output on huge corpuses of existing pieces, which in and of itself is an issue with borrowing style inherently leading to latent copying. More recent work in computational creativity has suggested that originality is a continuous and more contextual property for AI-generated art, one focusing less on visual novelty (largely practical given the combinatorial nature of digital generating processes), but instead arguing about levels/perception frameworks of transformation [29] or degrees of contextually reframed similarity/conceptual framing between source/target image pairs.

Although there are many articles being written on the topic, we need to clarify how AI-generated art can not be confused with plagiarising other artists' work or simply creating an homage to a specific artist's style. To fill this gap, the present study builds on both art-theoretical notions of originality and AI-derived methods for production in a detailed typological framework of replication, derivation and creative transformation. This in turn provides a much-needed perspective that explains how the originality of AI art cannot be evaluated using standard criteria alone.

Methodology : The present study employs a qualitative, interpretive research approach that integrates conceptual analysis with comparative case studies. The research corpus is primarily defined by generative models, where we compare AI-generated artworks with human-created counterparts that share a common visual or stylistic similarity. We use purposeful sampling to choose cases that have encouraged public or academic contention about originality and copying.

Data collection consists of (1) Artworks analysis by visual and stylistic constituent, (2) Document analyses on artist statements, dataset disclosures and platforms guidelines then turn into semi-structured expert evaluations with artists/designer/academics working in Digital art+AI. The original assessment rubric—is structured based on transformation theory, aesthetic deviation and contextual intention as the main framework of analysis.

This methodology is also appropriate, as it allows for the study of context-dependent concepts — originality and creativity in this case since these are subjective measures and cannot be mostly quantified; hence rich qualitative data needed to cover all aspects.

Results and Conclusion: The results reveal that AI art is in a complicated middle ground between original creation, imitation (plagiarism), or gray content. A visual analysis shows that many of the AI artworks are stylistically similar, at least superficially speaking; however---through reassemblage, unexpected juxtapositions and new aesthetic paths--significant compositional reframing arises in several cases. Expert assessments imply that innovation with respect to AI Art is more properly conceived of as emergent originality—that emerges out of a dialectic between algorithm, training data and human intervention.

The paper also identifies which aspects of a generating dataset an individual has the most significant influence over what counts as copying, including transparency of datasets, specificity of prompts and amount that another human does or doesn't curate. Creations spun with learned stylistic cues are evaluated largely as derivative, but those that use parsing words born from conceptual or critical contexts can be easily accepted relative to an art object.

They confirm theories of postmodernity that reject absolute Originalität and subvert laws / ethical doctrines still rooted in personality-oriented proofs! The point here is that originality in AI art isn't so much about visual similarity, it's more a matter of intent and transformation and meaning.

Conclusion and Recommendations: The lines that define originality, copy culture or digital plagiarism differ largely between AI-generated art products in terms of conceptual not only technical ground. By removing the authorship of their works from humans, AI art fundamentally challenges traditional definitions around what creativity is — and its originality becomes a relational as well as process-based phenomenon. Our work adds to the literature by presenting an organized approach that can be used in evaluating originality for AI art, while considering transformation, intent and contextualization.

Potential contributions of this study may include a clearer framework for evaluative criteria that artists, educators and curators can implement in their practice with AI tools. For practitioners, if datasets and the creative process were better disclosed this strengthens claims of originality. For researchers, future studies can investigate empirical audience perceptions and legal interpretations of AI originality in diverse cultures. Ultimately, this research highlights the importance of adaptable theory that can show creativity in artificial intelligence.