The paper proposes dynamic-reflexive tracking (DRT), which requires that a creator's evolving reasons undergo reflective uptake, exert genuine influence on the subsequent trajectory of creation, and remain capable of rejecting and redirecting the system's default direction.
Abstract
Generative artificial intelligence (GenAI) significantly expands creators'productive capacity, but this does not necessarily entail a corresponding increase in creative agency or authorship. This paper distinguishes creativity at the level of the work from creative agency at the level of the creator, and argues that human authorship cannot be determined solely by manual intervention, degree of automation, the origin of an initial idea, or final selection authority. Rather, authorship depends on whether human judgment and reasons genuinely shape the development of the work. To articulate this requirement, the paper introduces Meaningful Human Control (MHC) into generative creation and identifies a limitation of its classical tracking condition. Creative reasons are not always fully specified prior to interaction with AI; they may emerge, change, or be abandoned as the creative process unfolds. The paper therefore proposes dynamic-reflexive tracking (DRT), which requires that a creator's evolving reasons undergo reflective uptake, exert genuine influence on the subsequent trajectory of creation, and remain capable of rejecting and redirecting the system's default direction. DRT consists of four conditions: diachronic reason formation, reflective uptake, trajectory efficacy, and contestability and redirection, together with a minimal tracing requirement. The paper argues that human authorship under generative AI depends not on how many steps a person personally performs, but on whether that person's reasons continuously, reflectively, and effectively shape what the work becomes.
It is concluded that resistance to attributing creativity to GenAI reflects genuine conceptual distinctions alongside persistent anthropocentric assumptions, and that current deployment practices carry ethical implications that demand an institutional response rather than case-by-case management.
A theoretical map of the main positions in the existing literature is offered, organized around three conceptual thresholds that structure the authors' perception of authorship in AI-generated works, highlighting both their argumentative strengths and limitations.
The answer lies not in deciding whether a machine can be an author but in reforming copyright to separate authorship, creative contribution and ownership, and a tiered framework calibrated to the degree of human creative control is proposed.
Manisha J. Singh, Utkarsh Chadha· International Journal of Law...· 0 citations
It is argued that “AI authorship” is a category mistake: statistical systems cannot occupy positions of accountability, vulnerability, and justificatory dialogue within socio-technical assemblages whose conditions of possibility lie in data extraction, platform governance, and planetary logistics.
Michael Uebel, B. Hamamra· Philosophy & Technology· 1 citation
It is argued that current generative AI systematically favors combinatorial creativity while offering weaker support for transformational creativity, and that HCI should evaluate AI tools not only by the quality of creative outputs they enable but by the distribution of creative types they support, privilege, or suppre...
Peter Dalsgaard· Proceedings of the 14th Nord...· 0 citations
A five-year interview study with 17 Chinese digital painters, based on annual semi-structured interviews from 2021 to 2025, frames these accounts as longitudinal agency partitioning, the situated work of deciding which stages, responsibilities, values, and claims remain human in creative human-agent interaction.
Yi-Bo Meng, Rui-Qi Chen, Shu-Heng Cao et al.· 0 citations
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