← 研踪 PaperTrailarXiv:2611.06660 · v1
Perceived Trust and Transparency in AI-Altered vs. Human-Written LinkedIn Posts
首次提交 2026-11-04 23:35
版本更新 2026-11-04 23:35
雷达观测 2026-11-05 12:13
摘要
People increasingly draft a social media post themselves and then let an AI model refine it (AI-altered), a common but understudied middle case between fully human-written and fully AI-generated content. We ask whether this changes how trustworthy and transparent readers perceive the post to be, and whether they can even tell. We first analyze real ChatGPT conversations (WildChat) to find how people actually alter social media posts, which we use to design realistic alteration prompts. We then run a pre-registered, within-subjects online experiment (n=214) in which participants rate unlabeled human-written and AI-altered LinkedIn-style posts for perceived trustworthiness and transparency, and separately judge whether each post was AI-altered. We find that AI-alteration itself has no detectable effect on either perceived trustworthiness or transparency (both estimates near zero, not significant). At the same time, readers did not reliably detect AI-alteration (49\% accuracy, near chance) and defaulted to assuming human authorship when unsure. However, our analysis shows significant differences in transparency and trustworthiness ratings across the specific post, the AI model, as well as the prompt used to alter it. For instance, more detailed prompts raise both perceived trustworthiness and transparency but also make the alteration more detectable. Surprisingly, the prompt that shifted ratings most also asked the model to add one new idea absent from the original post: readers nonetheless rated this AI-invented, unverifiable content as more trustworthy and transparent.
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v1 · 2026-11-04 23:35 · 最新观测版本
Perceived Trust and Transparency in AI-Altered vs. Human-Written LinkedIn Posts
People increasingly draft a social media post themselves and then let an AI model refine it (AI-altered), a common but understudied middle case between fully human-written and fully AI-generated content. We ask whether this changes how trustworthy and transparent readers perceive the post to be, and whether they can even tell. We first analyze real ChatGPT conversations (WildChat) to find how people actually alter social media posts, which we use to design realistic alteration prompts. We then run a pre-registered, within-subjects online experiment (n=214) in which participants rate unlabeled human-written and AI-altered LinkedIn-style posts for perceived trustworthiness and transparency, and separately judge whether each post was AI-altered. We find that AI-alteration itself has no detectable effect on either perceived trustworthiness or transparency (both estimates near zero, not significant). At the same time, readers did not reliably detect AI-alteration (49\% accuracy, near chance) and defaulted to assuming human authorship when unsure. However, our analysis shows significant differences in transparency and trustworthiness ratings across the specific post, the AI model, as well as the prompt used to alter it. For instance, more detailed prompts raise both perceived trustworthiness and transparency but also make the alteration more detectable. Surprisingly, the prompt that shifted ratings most also asked the model to add one new idea absent from the original post: readers nonetheless rated this AI-invented, unverifiable content as more trustworthy and transparent.
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