arXiv:2607. 15027v1 Announce Type: new Abstract: The integration of AI-driven systems in creative work has sparked debates among artists and legal communities about notions of ownership.
arXiv:2607. 17940v1 Announce Type: cross Abstract: This paper frames Generative Artificial Intelligence (AI) not as an unprecedented technological rupture, but as an industrial-scale manifestation of a deeply rooted historical process.
arXiv:2601. 15797v2 Announce Type: replace Abstract: Many theorists maintain that conscious intentional agency is a necessary condition of creativity.
The study investigates when work done with AI feels like one's own, using a qualitative survey where participants described tasks that felt owned versus not owned. Findings show that ownership depends on the collaboration process: people feel ownership when they lead, iterate, or rewrite, but disown work when merely approving AI suggestions. Ownership also extends to tasks where people set the vision but rely on AI for execution, yet loss of personal voice and lack of comprehension erode ownership, and willingness to disclose AI use is driven more by community norms than by pride.
arXiv:2606. 26114v1 Announce Type: cross Abstract: We examine the structural transformation of creative industries under generative artificial intelligence, drawing on 374 primary sources spanning policy documents, industry data, creator surveys, and platform analytics.
The paper examines how AI involvement in human collaboration can lead to unowned judgment, where decisions shaped by AI lack a clear accountable human or institution. Through cases of AI-assisted peer review and concealed AI use in creative work, it shows that contribution dissolution weakens responsibility and that fear of losing credit can deter disclosure. The authors argue for clearer distinctions of AI roles, identification of judgments needing human ownership, and conditions that allow disclosure without penalty, aiming to make AI-shaped contributions discussable, creditable, contestable, and repairable.
arXiv:2607. 08331v1 Announce Type: cross Abstract: Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production.
Communities often respond to potentially AI-assisted work by asking three questions: Was AI used? Was that use disclosed? Can hidden use be detected? These questions place AI use itself at the center...
Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production. While recent generative AI systems can synthesize artworks with high fidelity, they primarily model distributions over finished artifacts rather than the creative processes underlying their creation.
arXiv:2608. 14405v1 Announce Type: cross Abstract: Art style is a signature of professional digital artists that develops through repeated experimentation, reflection, and adaptation.
arXiv:2607. 20796v1 Announce Type: new Abstract: This paper examines the question of whether artificial intelligence (AI) systems can be creative, approached from the dual perspective of a researcher trained in electrical engineering, pattern recognition, machine learning, and neural networks, who has also spent most of his life engaged in the arts as actor, stage and film director, writer, composer, and visual artist, and in philosophy.
arXiv:2607. 28644v1 Announce Type: cross Abstract: Creativity in computational systems is often evaluated as an objective property of artifacts, with existing Computational Creativity (CC) frameworks assessing creative merit at the level of outputs or systems rather than interpretive context.
arXiv:2606. 30561v1 Announce Type: new Abstract: Modern AI evaluation frameworks treat evaluator disagreement as noise to be resolved.