Authorship After Generative AI: Distributed Creativity and Relational Responsibility
TL;DR
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.
Abstract
Generative AI exposes the historical fragility of Romantic myths of the sovereign, self-transparent writer without inaugurating a crisis of authorship itself. Tracing a genealogy from symbolic AI to large language models, we show how creativity has always depended on distributed infrastructures, archives, and labour that the figure of the solitary author conceals. Drawing on Barthes, Foucault, Butler, Haraway, Hayles, and recent legal and bibliometric debates, we argue that “AI authorship” is a category mistake: statistical systems cannot occupy positions of accountability, vulnerability, and justificatory dialogue. They operate as powerful catalysts within socio-technical assemblages whose conditions of possibility lie in data extraction, platform governance, and planetary logistics. On this basis, we advance a relational conception of authorship as ethical and epistemic stewardship over hybrid writing systems, contending that even under conditions of distributed agency and technical opacity, identifiable human agents must remain answerable for AI-mediated outputs. We conclude by sketching implications for copyright doctrine, contributorship taxonomies, and research ethics in an era of pervasive generative automation.