When Algorithms Meet Artists: Topic Modeling the AI-Art Debate, 2013-2025
It addresses the marginalization of artists' voices in AI-art debates, providing a methodology and baseline for future research, though it is incremental as it applies existing topic modeling to new data.
This study analyzed 12 years of English-language discourse on AI-generated art using 439 curated excerpts, identifying five thematic clusters and revealing a misalignment between artists' concerns and media narratives, with findings showing how technical jargon can sideline urgent issues like consent and creative labor.
As generative AI continues to reshape artistic production and alternate modes of human expression, artists whose livelihoods are most directly affected have raised urgent concerns about consent, transparency, and the future of creative labor. However, the voices of artists are often marginalized in dominant public and scholarly discourse. This study presents a twelve-year analysis, from 2013 to 2025, of English-language discourse surrounding AI-generated art. It draws from 439 curated 500-word excerpts sampled from opinion articles, news reports, blogs, legal filings, and spoken-word transcripts. Through a reproducible methodology, we identify five stable thematic clusters and uncover a misalignment between artists' perceptions and prevailing media narratives. Our findings highlight how the use of technical jargon can function as a subtle form of gatekeeping, often sidelining the very issues artists deem most urgent. Our work provides a BERTopic-based methodology and a multimodal baseline for future research, alongside a clear call for deeper, transparency-driven engagement with artist perspectives in the evolving AI-creative landscape.