研究作者背景如何影响AI文本检测准确率，发现语言水平和环境是关键因素。
Who Writes What: Unveiling the Impact of Author Roles on AI-generated Text Detection
- 分析性别、语言水平等作者特征对检测器的影响
- 语言水平和语言环境显著降低检测准确率
- 为公平可靠的AI检测系统提供实证依据
大语言模型（LLMs）的兴起要求精准的AI生成文本检测。然而，现有方法普遍忽视作者特征的影响。本文研究了社会语言学属性——性别、CEFR语言水平、学术领域和语言环境——对先进AI文本检测器的影响。基于ICNALE人类撰写文本语料库及多种LLM生成的平行文本，采用多因素方差分析和加权最小二乘法（WLS）进行严格评估。结果表明：语言水平和语言环境对检测准确率有显著影响；性别与学术领域的影响则依赖于具体检测器。这些发现凸显了在真实、跨域场景中发展社会敏感型检测系统的必要性，以避免对特定群体产生不公平判罚。本文提供了新的实证证据、稳健的统计框架和可操作洞见，推动未来在偏差缓解、包容性评估基准和社会责任导向的LLM检测器方面的研究。
原文摘要 · Abstract (English)
The rise of Large Language Models (LLMs) necessitates accurate AI-generated text detection. However, current approaches largely overlook the influence of author characteristics. We investigate how sociolinguistic attributes-gender, CEFR proficiency, academic field, and language environment-impact state-of-the-art AI text detectors. Using the ICNALE corpus of human-authored texts and parallel AI-generated texts from diverse LLMs, we conduct a rigorous evaluation employing multi-factor ANOVA and weighted least squares (WLS). Our results reveal significant biases: CEFR proficiency and language environment consistently affected detector accuracy, while gender and academic field showed detector-dependent effects. These findings highlight the crucial need for socially aware AI text detection to avoid unfairly penalizing specific demographic groups. We offer novel empirical evidence, a robust statistical framework, and actionable insights for developing more equitable and reliable detection systems in real-world, out-of-domain contexts. This work paves the way for future research on bias mitigation, inclusive evaluation benchmarks, and socially responsible LLM detectors.
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