研究发现，种族和国籍会影响对AI生成申请文书的判断，导致偏见。
Nationality, Race, and Ethnicity Biases in and Consequences of Detecting AI-Generated Self-Presentations
- 用语言风格等内容线索判断AI生成内容，国籍线索也显著影响判断。
- 亚裔和西裔国内学生更易被误判为用AI，体现种族刻板印象叠加。
- 误判会降低对申请人能力、品行和未来的评价，影响升学机会。
本研究基于人格感知与人机交互理论，探讨在大学申请这一高风险自陈述场景中，种族、族裔和国籍等来源线索与内容线索如何影响对AI生成内容的判断。一项预注册实验使用美国全国代表性样本（N = 644），结果显示：语言风格等内容启发式在识别AI生成内容中起主导作用；国籍等来源启发式同样显著，国际学生更可能被判定使用AI，尤其当其陈述包含AI特征时。有趣的是，亚裔和拉丁裔申请人若被标注为本土学生，反而更易被误判为使用AI，反映出种族刻板印象与AI检测的交互作用。将内容归因于AI后，评审者对其陈述质量、真实性以及申请人能力、社交性、道德品质和未来成功预期均产生负面评价。
原文摘要 · Abstract (English)
This study builds on person perception and human AI interaction (HAII) theories to investigate how content and source cues, specifically race, ethnicity, and nationality, affect judgments of AI-generated content in a high-stakes self-presentation context: college applications. Results of a pre-registered experiment with a nationally representative U.S. sample (N = 644) show that content heuristics, such as linguistic style, played a dominant role in AI detection. Source heuristics, such as nationality, also emerged as a significant factor, with international students more likely to be perceived as using AI, especially when their statements included AI-sounding features. Interestingly, Asian and Hispanic applicants were more likely to be judged as AI users when labeled as domestic students, suggesting interactions between racial stereotypes and AI detection. AI attribution led to lower perceptions of personal statement quality and authenticity, as well as negative evaluations of the applicant's competence, sociability, morality, and future success.
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