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

תקציר מקורי באנגליתarXiv:2508.03037v5 Announce Type: replace 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