When Algorithms Meet Artists: Semantic Compression of Artists' Concerns in the Public AI-Art Debate

TL;DR

This study reveals a significant underrepresentation of artists' concerns in public AI-art discourse, highlighting a semantic gap that affects AI governance decisions and introducing a new methodology for stakeholder analysis.

Contribution

It introduces a novel semantic projection method to quantify stakeholder concerns and demonstrates its application in revealing underrepresented artist perspectives in AI discourse.

Findings

95% of artist concerns cluster in 4 discourse topics

62% of discourse topics contain no artist perspective

Governance concerns are 7x underrepresented in public discourse

Abstract

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.