The Office concludes that human contributions to AI-generated outputs are sufficient to constitute authorship but must be analyzed on a case-by-case basis. It states that fully AI-generated outputs produced in response to a human input or prompt lack human authorship and are therefore not copyrightable. Human authorship is described as a longstanding principle of copyright law, with copyright protection in the age of AI remaining tied to human creativity, even when authors are aided by AI tools.

It concludes that the outputs of generative AI can be protected by copyright only where a human author has determined sufficient expressive elements. This can include situations where a human-authored work is perceptible in an AI output, or a human makes creative arrangements or modifications of the output, but not the mere provision of prompts. The Office confirms that the use of AI to assist in the process of creation or the inclusion of AI-generated material in a larger human-generated work does not bar copyrightability.

Scientific authorship is undergoing a subtle but profound transformation. With the rise of generative artificial intelligence (AI), the boundary between human contribution and machine assistance is increasingly blurred. This article explores how AI-assisted scientific writing challenges traditional definitions of authorship, accountability, and intellectual ownership. It argues that the growing role of AI in manuscript production demands a reconsideration of contributor roles, transparency, and recognition.

Allowing that human authors may use AI in the creative process, the AI Guidance states that “what matters is the extent to which the human had creative control over the work’s expression.” When AI “determines the expressive elements of its output, the generated material is not the product of human authorship.” Given current generally available technology, prompts alone do not provide sufficient human control to make users of an AI system the authors of the output, and authors may claim copyright protection only for their own contributions to works that include AI-generated material.

Guidance from the US Copyright Office establishes that, under current US law, there is no ownership of AI-generated works by anyone – not by the authors of the AI tool, not by the tool itself and not by the individual who enters the prompts to generate the work. As such, these works are currently considered to be in the public domain, without copyright protection. However, the Office explains that a work containing AI-generated material will also contain sufficient human authorship to support a copyright claim where an author makes creative arrangements or substantially modifies AI-generated works, and in these cases copyright will only protect the human-authored aspects of the work.

Recent developments in generative artificial intelligence pose challenges to traditional legal concepts, one of which is authorship. But the goal of this paper is the revision of the conception of authorship that underlies the international norms that regulate intellectual property and the ethical assumptions that justify them (Locke’s conception, the romantic conception and the utilitarian conception). Based on a reading of Michel Foucault’s “What is an author?”, it is argued that the notion of authorship fulfills a social function that, at least to the state of the art of generative artificial intelligence, it cannot be fulfilled by it and, therefore, generative artificial intelligence should not be recognized as the author of the mentioned works.

Copyright law traditionally acknowledges only human authorship, excluding works produced by animals or machines. Despite AI systems generating increasingly sophisticated content, they remain unrecognized as authors. The widespread use of generative AI (gen-AI) has intensified debates on human authorship and AI-assisted works. Particularly when AI tools are used for augmentation rather than replacement of human creative work, it is unclear when such work qualifies as human-authored under copyright law, making AI-assisted outputs a legal grey area.

Under well-established case law, the Guidance explained, “the term ‘author,’ used in both the Constitution and the Copyright Act, excludes non-humans.” In the context of generative AI, this means that “[i]f a work’s traditional elements of authorship were produced by a machine, the work lacks human authorship and the Office will not register it.” The Guidance instructs applicants seeking to register works containing more than de minimis AI-generated content to limit their claims to human-authored aspects and to disclaim AI-generated portions.

Employing a thematic synthesis approach within a critical sociotechnical framework, the analysis identifies four dominant, interrelated themes: (1) the reconfiguration of authorship and attribution, (2) the transformation of pedagogy and assessment, (3) the evolving dynamics of integrity, trust, and detection, and (4) the emergent ethical and sociopolitical ramifications. Empirical studies reveal that the *practice* of authorship is being fundamentally reconstituted as a tacit, hybrid, human–AI collaboration. This systematic critical review has documented the profound and multifaceted impact of generative AI on the scholarly enterprise from 2023 to 2025. The evidence confirms that GenAI is not a peripheral tool but a *constitutive force* actively reshaping authorship, pedagogy, integrity, and the very ethics of knowledge production. First, the literature reveals that authorship is not being replaced but reconfigured, with formal policy prohibitions masking the practical normalisation of hybrid human–AI collaboration.

The rapid spread of generative AI has renewed and reframed classic problems in aesthetics, critical theory, and semiotics concerning authorship, intention, originality, agency, and attribution. Generative AI reopens these debates in intensified form: when production becomes computationally mediated and massively recombinatory, the author becomes harder to locate and creativity appears less as a personal capacity than as an emergent property of workflows, datasets, models, and socio-technical infrastructures. The law often presupposes relatively stable notions of authorship and creative contribution, yet generative AI makes these assumptions increasingly difficult to sustain. It pushes toward new models of authorship, ownership, and liability.

Generative AI creations have brought about an AI crisis of originality. Generative AI directly challenges this anthropocentric principle. Accordingly, outputs generated entirely by AI without substantial human modification should be placed in the public domain, while protection may only attach to human-authored post-production elements or highly creative, iterative forms of prompting. Originality is not granted to the raw AI output but to the modifications, editing, and creative additions made by humans after the AI generates the work.

The study surveys laypeople’s views on copyright and authorship for AI-generated art and finds: "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." It also notes that "judgments concerning the AI model were more uncertain, with evaluators, on average, neither agreeing nor disagreeing that the AI model itself is an author," and that "the company that developed the AI model had the lowest perceived authorship." The authors conclude that people see creativity and effort as needed to create AI-generated art, but not traditional skills, and that public views diverge from existing legal notions of authorship and copyright.[1][5]

Users of generative AI tools: The prevailing view is that copyright, if granted, would most likely belong to the natural person who actively uses the AI tool in their creative process. If a user provides highly detailed and specific instructions, input and prompts, effectively shaping the AI-generated output to reflect their intended creative vision, they could be recognised as the author. Developers and owners of AI tools are generally not considered authors of outputs generated by their software, as they do not exercise sufficient creative control over individual outputs.

Generative artificial intelligence (AI) fundamentally challenges the traditional conception of authorship within copyright law. It was long thought that only human creativity can generate copyrightable works. Recent advances enable the production of expressive content with minimal or no direct human input, raising complex questions regarding originality, creative contribution, and attribution. However, the swift expansion of AI-assisted and AI-generated works has exposed significant doctrinal ambiguity regarding originality, creative contribution, and attribution. The Article proposes a control-based framework for copyright authorship, arguing that substantial human creative control, rather than mere mechanical expression, should be the primary criterion for authorship attribution in generative AI-mediated creation.

In this paper, I consider whether works produced by means of users inputting natural language prompts into Generative Adversarial Networks are works of authorship. I argue that they are not. This is not due to concerns about randomness or machine-assistance compromising human labor or intellectual vision, but instead due to the syntactical and compositional limitations of existing AI systems in handling natural language prompts. This, I argue, gives rise to ‘authorship gaps’.

This study aimed to determine how generative algorithms have transformed conceptions of authorship and ownership in contemporary art, and what ethical consequences arose from such transformation. In conclusion, the findings presented in this subsection demonstrate that the concept of authorship, though increasingly complex in the era of generative AI, continues to hinge on the presence of human intention, accountability, and cultural embeddedness. These results confirmed that AI blurs the boundaries of authorship and artistic originality, generating novel ethical and conceptual tensions within contemporary art discourse. Generative AI compels to rethink foundational categories—authorship, originality, ownership—while also reaffirming the centrality of human meaning-making in artistic practice.

The outputs of generative AI models challenge traditional notions of authorship and originality. Under EU law, works generated entirely by machines without human intervention do not benefit from copyright protection. However, many outputs emerge from iterative human use of algorithmic tools, raising questions about authorship boundaries and leading to legal uncertainty and fragmentation across the internal market regarding hybrid authorship.

While publishers and editors have swiftly reached a consensus position that AI tools do not qualify for authorship because they cannot take responsibility, manage conflicts of interest, or hold copyright, a more insidious problem is emerging: the new ghost in the scholarly machine. The true crisis lies in the undeclared, pervasive use of large language models (LLMs) to generate substantive portions of a manuscript, e.g., the literature review, the portions of methodology, the discussion, or the framing of a conclusion. When a researcher submits an article that is significantly drafted by an LLM without clear disclosure, they are effectively engaging in a contemporary form of ghost authorship. It fundamentally breaches the core principle that academic authorship must honestly and accurately reflect contributions and accountability. However, AI should not be credited as a co-author. To mitigate the threat of AI ghost authorship, the scholarly community needs to move past simple prohibition and toward mandated transparency, which the APA is now recommending for authors.

In the third section, I present examples of entities generated by GAI and argue that these entities are indeed artifacts for which no proper authorship claim can be justified based on existing theories of authorship. Generative AI challenges the conceptual foundations of authorship by producing artifacts that lack a clear agent who can be held responsible for their content or form. This creates a philosophical tension between our practices of attributing authors and the way GAI systems operate.

Its main conclusions were: the use of AI tools to assist human creations of new works of authorship does not affect copyright in those works; copyright is available for original expression of human authors, even if the work includes some AI outputs; copyright is not available for purely AI-generated material; although purely AI-generated material is not copyrightable for lack of human authorship, a creative selection and arrangement of the outputs or creative modifications of them by human authors may be; and the Office will make registration decisions on a case-by-case, work-by-work basis. These principles reflect the Office’s view that, for a work to be copyrightable, there must be an identifiable human author whose creative choices are expressed in the work, which remains critical even in the context of generative AI.

Reporting on a study in AI & Society titled "Revisiting Computer Authorship: A Longitudinal Perspective," this article explains that public understanding of computer authorship has remained "fragmented, hesitant, and conceptually unstable" between survey waves in 2017–2018 and 2024–2025. It states that "participants in the later study did not converge on a clearer or more settled definition of authorship when confronted with computer-generated text" and that responses "continued to reflect ambiguity over whether authorship can be meaningfully attributed at all in such cases." At the same time, respondents showed "increased readiness to attribute authorship directly to the system itself once AI involvement was disclosed," while others resisted attribution entirely.[2]

In a mixed-method survey experiment (N = 602) on generative AI creative writing assistance, the authors examine perceptions of authorship, creatorship, responsibility, and disclosure. They report that for a human primary author "the degree of assistance they receive matters for our assessments of their level of authorship, creatorship, and responsibility, but not what or who rendered that assistance." However, "in our assessments of the assisting agent, human assistants were viewed as warranting higher rates of authorship, creatorship, and responsibility, compared to AI assistants rendering the same level of support." The paper directly asks whether "ChatGPT can be an author" and shows people systematically assign less authorship-like credit to AI helpers than to human helpers for equivalent contributions.[10]

The relevance of the topic is due to radical changes in the field of cultural production, where generative AI systems create texts, images and ideas, which forces us to rethink the very concepts of creativity, originality and intertextuality. The concept of "death of the author" acquires a new meaning, a new dimension of "network authorship", which changes the idea of creating meanings, where the author is already a collective, and the creative process is distributed between man and technology. Today, in the era of the spread and active development of artificial intelligence systems, the concept of "author" is significantly expanded and includes both inspiration and algorithmic replication of cultural codes.

This survey study of knowledge workers (N = 155) investigates "Which Contributions Deserve Credit?" in collaborations with AI vs. human partners. The authors find "a consistent pattern in which AI was assigned less credit for equivalent contributions" when compared to a human partner. Participants considered disclosure of AI involvement important and drew on quality of contribution, personal values, and technology considerations when making attribution judgments. The paper situates its results within prior work showing that "people’s perceptions of AI authorship in co-creation are nuanced" and that different contribution types and degrees of initiative influence who is seen as deserving authorship-like credit.[12]

The advent of generative Artificial Intelligence (AI) has brought about a significant shift in the way works are created, with the blurring of boundaries between human and machine-driven creation processes becoming a prominent challenge. In AI-assisted creation, traditional notions of authorship are disrupted because the contribution of the human author is interwoven with algorithmic processes that may introduce elements beyond the author’s direct control. This Article explores how these developments complicate the identification of the author and suggest that authorship must increasingly be understood as a relational and distributed concept rather than a singular, human-centered one.

The US Congress and Federal Courts have interpreted the "Writings of Authors" clause as being limited to works "created by a human being", declining to grant copyright to works generated without human intervention. The Copyright Office further clarified in January 2025 that AI-assisted works where the creative expression of the human remains evident in the work can be copyrighted, which can include creative adaptation of prompts for AI generators or usage of AI to assist in the creation process, such as in filmmaking. Works "where the expressive elements are determined by a machine" still remain uncopyrightable.

Generative Artificial Intelligence for text is reshaping our writing practices. What was once a solitary process is now a dialogue with intelligent machines. This change is so profound that we now perceive ourselves as hybrid authors, with a capacity for creation amplified by AI. The emergence of GAI challenges the traditional idea that the author is a single, autonomous subject who alone is responsible for the text. Instead, authorship becomes a shared, iterative process in which human and machine contributions are intertwined.

This article in the Journal of Interactive Computing and Ethics in Society analyzes attitudes of artists and technologists toward AI-generated art, focusing on creativity, authorship, ownership, and social impact. It reports that "authorship and market-impact scores did not differ significantly" between groups, but that there were "large gaps" for ownership and public perception. The study finds that artists tended to favor limited copyright and lower prices for AI art, whereas technologists more often endorsed full protection and price parity under specified conditions, indicating differing normative views on how AI-generated works should be owned and valued.[4]

At the moment, works created solely by artificial intelligence — even if produced from a text prompt written by a human — are not protected by copyright in the United States. It has long been the posture of the U.S. Copyright Office that there is no copyright protection for works created by non-humans, including machines, and therefore the product of a generative AI model cannot be copyrighted. If a human simply types in a prompt and a machine generates complex works in response, the “traditional elements of authorship” have been executed by AI, a non-human, so fully AI-generated art cannot be copyrighted.

This article therefore posits the view that AI redefines authorship as a continuum of human-AI interactions, emphasizing co-creation and expanded paratexts as pivotal concepts. AI-generated texts and images that mimic human creativity thereby necessitate a re-evaluation of the concepts of authorship, originality, and the intrinsic value of cultural artifacts. The article argues that instead of viewing AI as either a mere tool or an autonomous creator, we should understand authorship as dispersed across networks of humans and machines, with paratextual elements (disclosures, prompts, metadata) becoming central to how works are framed and valued.

It argues that the users who significantly and substantively incorporate GenAI tools in their creative process deserve authorship recognition. Specifically, it argues that the personality theory of copyright endorses the view that incorporating GenAI in the creative process enhances an author’s ability to express themselves. Under this view, generative AI becomes part of the author’s extended means of self-expression rather than an independent creator.

Using corpora of Reddit posts and U.S. Copyright Office publications, this study explores "Perceptions of Authorship of Artificial Intelligence-Generated Content." It identifies four key attributes relevant for determining human authorship over AI-generated works in public and official discourse. The authors show how lay discussions on Reddit grapple with questions of who should be credited as author when AI systems contribute to creative output, and contrast these with more formal criteria articulated in copyright policy documents, highlighting a tension between evolving social intuitions and relatively stable legal standards.[3]

This master’s thesis examines "layuser perceptions of creative ownership" for AI-generated content via an online survey (n = 264). It reports that "the general sentiment of the surveyed online population trend sharply negative, with output from GenAI tools being largely identified as not inducing feelings of personal ownership." The study analyzes scenario-based perceptions of ownership across different types of generative systems and asks how users see their own and peers’ ownership over AI outputs, finding that many respondents hesitate to claim creative ownership of content produced with generative tools.[8]

In a post summarizing comparative copyright approaches, the author explains that in the United States and many other jurisdictions "for such content to be copyrightable, most jurisdictions, including the United States, require some level of human creativity or originality in the selection and/or modification of the AI-generated content." It notes that "AI-generated work, alone—in response to a human user’s prompt—is not afforded copyright protection" and cites the U.S. Copyright Office’s position that "prompts alone do not provide sufficient human control to make users of an AI system the authors of the output." Courts in multiple countries are described as rejecting the idea that a simple prompt is enough to constitute authorship.[15]

Generative AI has forced long-simmering questions about copyright, ownership, and authorship into the spotlight. Courts, regulators, and scholars are now grappling with a deceptively simple issue: *who* is the author of content created with AI? For the open access community, the deeper issue isn’t just ownership. It’s how generative AI challenges the assumptions that open access has relied on for decades: identifiable authors, clear rights, meaningful attribution, and stable systems of scholarly communication. In other words, AI doesn’t just complicate the rules—it reshapes the playing field. At the same time, it destabilizes the norms—authorship, attribution, accountability—that made open access workable in the first place.

In this experimental study on reader perceptions when AI authorship is disclosed, the authors analyze 990 responses and find that "disclosure generally erodes perceptions of trustworthiness, caring, competence, and likability, with the sharpest declines in social and interpersonal writing." They connect these shifts to "a perceived loss of human sincerity, diminished author effort, and the contextual inappropriateness of AI." The paper synthesizes prior work noting that readers often see AI-generated texts as less empathetic or authentic, and that disclosure of AI involvement can degrade perceived authenticity and trust.[7]

Material created by generative AI tools does not currently receive copyright protections in the United States. Currently, copyright protection is not granted to works created solely by Artificial Intelligence; projects that incorporate both material generated by AI and material originally created by the author can receive copyright protection, but any components of the work that were created by generative AI are still uncopyrightable. The U.S. Copyright Office has issued guidance that explains the requirement for human authorship and provides information to creators working in tandem with AI tools on correctly registering their works.

This open-access article investigates "Understanding and Perception of Automated Text Generation" among German-speaking participants. It reports "a preference for human authorship across a wide range of topics and a lack of knowledge concerning the function, data sources, and responsibilities of ATG." Participants particularly refused to read AI-generated texts about "genuinely human-centered topics (e.g., society, culture, or politics)." When asked directly about author preferences, respondents "prefer human authorship across all 18 topics," even as the second study showed slight shifts further toward human preference, indicating persistent skepticism toward AI as an authorial agent.[6]

Across seven pre-registered studies on AI-generated marketing communications, the authors document an "AI-authorship effect." They find that using generative AI to craft emotional messages, as opposed to a human, "leads to a decrease in positive word of mouth and consumer loyalty." Consumers "tend to exhibit lower levels of positive word of mouth and brand loyalty when they suspect that emotional marketing messages are generated by AI rather than by a human author." Communications believed to be AI-generated are regarded as "less authentic" and evoke "heightened moral disgust," which mediates these negative responses.[14]

Copyright law currently has a human authorship requirement, and according to recent guidance, when an AI technology "determines the expressive elements of its output, the generated material is not the product of human authorship." What this means is that AI-generated art and text is not copyrightable on its own. Authors may claim copyright for human-authored portions of works that incorporate AI-generated material, but must distinguish between their creative contribution and machine-generated content.

The notion of authorship, already destabilized by poststructuralists, faces fresh challenges in the age of algorithmic writing. Journals such as Nature explicitly prohibit granting authorship to AI, noting that authorship requires forms of responsibility that LLMs cannot fulfill. These findings, I assert, suggest that AI has not replaced human authorship but is reshaping it. Technological change has never extinguished humanity’s drive to create; rather, it alters the forms creativity takes.

In a recent court case, 'Thaler v. Perlmutter,' a U.S. District Court found that work autonomously generated by an AI model is not copyrightable. U.S. Copyright law requires human authorship; if there is significant creative human involvement in the creation or editing of the AI-generated work, it may qualify for copyright protection. The case underscores that autonomously generated AI works fall outside copyright, while hybrid works with substantial human creative input may be protected.

When discussing the extent to which a human can use generative AI tools to write and still claim authorship, and at what point they should disclose their usage, both experts find the answer is context dependent. “Whether a person can call themselves an author depends on their own judgement of their contribution,” says Merve. “However, the more just approach is where publishers draw the line.” The article describes how generative AI tools are increasingly embedded in writing and publishing workflows and notes that norms around disclosure, credit, and responsibility are still evolving, leading to uncertainty over what counts as being an "author" in AI-mediated creation.

This experimental work on the "Effects of Assumed AI vs. Human Authorship" shows that simply labeling a text as AI-written, compared with human-written, "reduced perceived message credibility (d = 0.36)." It also reports that "AI authorship decreased perceived source credibility (d = 0.24), anthropomorphism (d = 0.67), and intelligence (d = 0.41)." The results indicate that assumptions about AI vs. human authorship systematically shape how readers evaluate the text and its putative author, even when the content is held constant.[13]

The latest incarnation of these trends under Generative AI – “computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data” – would seem to rewrite the rule book. By automating the production of complex, creative outputs, generative systems problematize long-standing assumptions about the singular author, the originality of works, and the boundaries between tool and creator. The paper argues that architecture and design, like other creative fields, must reconsider who or what is considered the author when AI systems are deeply involved in the generation and refinement of projects.

The Copyright Office has adopted an official policy declaring that it will “register an original work of authorship, provided that the work was created by a human being.” This raises the question of whether AI-generated content can be considered the work of a human being. In one sense, it is, yet the program usually generates content that no human being is responsible for, leaving the question largely unanswered. The article explains that generative AI challenges the concept of authorship because it can produce content that is not easily attributable to a specific human creator, complicating legal and philosophical understandings of who the "author" is.

AI and the Philosophy Machine: Authorship & Acknowledgement. The emergence of powerful generative AI acts as a "philosophy machine" not because it philosophizes itself, but because it forces us to confront and re-examine fundamental human concepts like authorship, creativity, intention, and acknowledgment. This contemporary challenge arises from AI's unprecedented ability to generate sophisticated text, images, and even code, blurring the lines of human intellectual and artistic production. Consider authorship: Traditionally, an author is the originator of a work, responsible for its unique content and expression. AI systems, by compiling, transforming, and generating novel content, complicate this. The conventional understanding of individual intellectual property and creative claim is thus profoundly challenged.