LLMs offer new creative possibilities for writers but also raise concerns about authenticity and reader trust, particularly when AI involvement is disclosed. Prior research has largely framed this as an issue of transparency and provenance, emphasizing the disclosure of human-AI interaction traces that account for how much the AI wrote and what the human did. Yet such audit-oriented disclosures may risk reducing creative collaboration to quantification and surveillance. In this position paper, we argue for a different lens by exploring how human-AI interaction traces might instead function as expressive artifacts that foreground the meaning-making inherent in human-AI collaboration. Drawing inspiration from blackout poetry, we frame AI-generated text as found material through which writers' acts of curation and reinterpretation become inscribed atop the AI's original output. In this way, we suggest that designing interaction traces as aesthetic artifacts may help readers better appreciate and trust writers' creative contributions in AI-assisted writing.
@article{arxiv.2604.09605,
title = {Human-AI Interaction Traces as Blackout Poetry: Reframing AI-Supported Writing as Found-Text Creativity},
author = {Syemin Park and Soobin Park and Youn-kyung Lim},
journal= {arXiv preprint arXiv:2604.09605},
year = {2026}
}
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4 pages, Accepted to ACM CHI 2026 Workshop on Herding CATs: Making Sense of Creative Activity Traces