Artists occupy a paradoxical position in generative AI: their work trains the models reshaping creative labor. We tested whether their concerns achieve proportional representation in public discourse shaping AI governance. Analyzing public AI-art discourse (news, podcasts, legal filings, research; 2013--2025) and projecting 1,259 survey-derived artist statements into this semantic space, we find stark compression: 95% of artist concerns cluster in 4 of 22 discourse topics, while 14 topics (62% of discourse) contain no artist perspective. This compression is selective - governance concerns (ownership, transparency) are 7x underrepresented; affective themes (threat, utility) show only 1.4x underrepresentation after style controls. The pattern indicates semantic, not stylistic, marginalization. These findings demonstrate a measurable representational gap: decision-makers relying on public discourse as a proxy for stakeholder priorities will systematically underweight those most affected. We introduce a consensus-based semantic projection methodology that is currently being validated across domains and generalizes to other stakeholder-technology contexts.

翻译：艺术家在生成式AI中处于矛盾地位：他们的作品训练着重塑创意劳动的模型。我们检验了他们的关切在塑造AI治理的公共话语中是否获得比例性代表。通过分析公共AI艺术话语（新闻、播客、法律文件、研究；2013-2025年）并将1,259份调查衍生的艺术家陈述投射至此语义空间，我们发现显著的压缩现象：95%的艺术家关切聚集在22个话语主题中的4个，而14个主题（占话语的62%）完全不包含艺术家视角。这种压缩具有选择性——治理关切（所有权、透明度）的代表性不足达7倍；情感主题（威胁、效用）在控制风格变量后仅显示1.4倍的代表性不足。该模式表明这是语义层面而非风格层面的边缘化。这些发现证明了一种可测量的表征差距：依赖公共话语作为利益相关者优先事项代理的决策者，将系统性地低估受影响最深的群体。我们提出一种基于共识的语义投射方法，该方法目前正在跨领域验证，并可推广至其他利益相关者-技术情境。