Unlimited Editions: Documenting Human Style in AI Art Generation

As AI art generation becomes increasingly sophisticated, HCI research has focused primarily on questions of detection, authenticity, and automation. This paper argues that such approaches fundamentally misunderstand how artistic value emerges from the concerns that drive human image production. Through examination of historical precedents, we demonstrate that artistic style is not only visual appearance but the resolution of creative struggle, as artists wrestle with influence and technical constraints to develop unique ways of seeing. Current AI systems flatten these human choices into reproducible patterns without preserving their provenance. We propose that HCI's role lies not only in perfecting visual output, but in developing means to document the origins and evolution of artistic style as it appears within generated visual traces. This reframing suggests new technical directions for HCI research in generative AI, focused on automatic documentation of stylistic lineage and creative choice rather than simple reproduction of aesthetic effects.

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Publication Details

Published
2025-04-23
DOI
https://doi.org/10.1145/3706599.3716214
Primary Topic
Aesthetic Perception and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Unlimited Editions: Documenting Human Style in AI Art Generation

Alex Leitch, Celia Chen
Aesthetic Perception and Analysis
article

Unlimited Editions: Documenting Human Style in AI Art Generation

Alex Leitch, Celia Chen
article en

Abstract

As AI art generation becomes increasingly sophisticated, HCI research has focused primarily on questions of detection, authenticity, and automation. This paper argues that such approaches fundamentally misunderstand how artistic value emerges from the concerns that drive human image production. Through examination of historical precedents, we demonstrate that artistic style is not only visual appearance but the resolution of creative struggle, as artists wrestle with influence and technical constraints to develop unique ways of seeing. Current AI systems flatten these human choices into reproducible patterns without preserving their provenance. We propose that HCI's role lies not only in perfecting visual output, but in developing means to document the origins and evolution of artistic style as it appears within generated visual traces. This reframing suggests new technical directions for HCI research in generative AI, focused on automatic documentation of stylistic lineage and creative choice rather than simple reproduction of aesthetic effects.

University of Maryland, College Park (US)
Openalex Percentile: Top 92%
Aesthetic Perception and Analysis
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