Human-AI Interaction Traces as Blackout Poetry: Reframing AI-Supported Writing as Found-Text Creativity
Syemin Park, Soobin Park, Youn-kyung Lim

TL;DR
This paper proposes viewing human-AI interaction traces as artistic artifacts, inspired by blackout poetry, to enhance appreciation and trust in AI-assisted writing rather than focusing on transparency metrics.
Contribution
It introduces a novel perspective by framing AI interaction traces as expressive artifacts, emphasizing creative reinterpretation over mere transparency.
Findings
Interaction traces can serve as aesthetic artifacts.
This approach highlights the meaning-making process in AI-assisted writing.
Designing traces as art may improve reader trust.
Abstract
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…
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