AI写作披露会遭惩罚，且对女性和黑人作者更不公平。
Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing
- 通过控制实验对比人类与大模型对披露AI使用的文章评分
- 无论人还是机器都降低披露文章的评分，但机器有身份偏见
- 机器更倾向未披露的女性或黑人作者，披露后优势消失
随着AI被广泛用于各类写作，关于透明使用AI的呼声日益增长。然而，若透明度要求对不同身份群体造成不平等负担，则开放成本将呈现不对称性。本研究探讨了AI披露声明如何影响写作质量评价，并考察其在作者种族与性别上的差异。通过大规模受控实验，1970名人类评估者与2520名大语言模型评估者对同一篇新闻文章进行评分，同时系统性地改变披露状态与作者背景。该方法反映了人类与算法决策共同影响机会获取（如招聘、晋升）与社会认可（如内容推荐）。结果表明，人类与大模型均对披露使用AI的文章给予更低评分。但仅大模型表现出显著的群体交互效应：在无披露时更青睐女性或黑人作者的作品，而一旦披露，这种优势即消失。研究揭示了AI披露与作者身份之间的复杂关系，凸显了机器与人类评价模式的差异。
原文摘要 · Abstract (English)
As AI integrates in various types of human writing, calls for transparency around AI assistance are growing. However, if transparency operates on uneven ground and certain identity groups bear a heavier cost for being honest, then the burden of openness becomes asymmetrical. This study investigates how AI disclosure statement affects perceptions of writing quality, and whether these effects vary by the author's race and gender. Through a large-scale controlled experiment, both human raters (n = 1,970) and LLM raters (n = 2,520) evaluated a single human-written news article while disclosure statements and author demographics were systematically varied. This approach reflects how both human and algorithmic decisions now influence access to opportunities (e.g., hiring, promotion) and social recognition (e.g., content recommendation algorithms). We find that both human and LLM raters consistently penalize disclosed AI use. However, only LLM raters exhibit demographic interaction effects: they favor articles attributed to women or Black authors when no disclosure is present. But these advantages disappear when AI assistance is revealed. These findings illuminate the complex relationships between AI disclosure and author identity, highlighting disparities between machine and human evaluation patterns.
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