When Algorithms Meet Artists: Semantic Compression and Stake-holder Marginalisation in Public AI-Art Discourse (2013-2025)

Abstract:Artists occupy a paradoxical position in generative AI. Their own work trains models that now compete with them, replicate their styles, and reshape the creative economy they inhabit. Yet whether artist concerns achieve proportional representation in the public discourse that shapes AI governance remains an open empirical question. We mapped the semantic landscape of public AI-art discourse from 2013 to 2025, drawing on 1,736 text chunks from news, podcasts, legal filings, and research, and projected 252 US-based practising artists' survey responses, captured across 70 unique frames spanning five concern dimensions, into the same space. We identify what we term semantic compression, the systematic narrowing of a diverse set of stakeholder concerns into a narrow region of public meaning-space. Compression is selective. Nearly all artist statements concentrate in just two of twenty discourse topics, while most of the remaining discourse volume sits in topics with no artist voice at all. Public discourse speaks about artists in regions where artists themselves are absent. Compression also operates inside the topics that do contain artists: dozens of distinct ownership and utility positions flow into a single topic framed as a debate about aesthetic authenticity, persisting after we control for differences in writing style between surveys and media. Public discourse amplifies broad tropes about creativity while attenuating the specific, actionable regulatory claims that artists make. Amid ongoing coalition mobilisation and litigation over AI training, these findings identify a structural pattern by which primary stakeholders are rendered peripheral in the very discourse that shapes their conditions of practice.

Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)

arXiv:2508.03037 [cs.CL]

(or arXiv:2508.03037v5 [cs.CL] for this version)