Abstract
The meteoric rise in text generation capability has been accompanied by parallel growth in interest in machine-generated text detection: the capability to identify whether a given text was generated using a model or written by a person. While detection models show strong performance, they have the capacity to cause significant negative impacts. We explore potential biases in English machine-generated text detection systems. We curate a dataset of student essays and assess 16 different detection systems for bias across four attributes: gender, race/ethnicity, English-language learner (ELL) status, and economic status. We evaluate these attributes using regression-based models to determine the significance and power of the effects, as well as performing subgroup analysis. We find that while biases are generally inconsistent across systems, there are several key issues: several models tend to classify disadvantaged groups as machine-generated, ELL essays are more likely to be classified as machine-generated, economically disadvantaged students' essays are less likely to be classified as machine-generated, and non-White ELL essays are disproportionately classified as machine-generated relative to their White counterparts. Finally, we perform human annotation and find that while humans perform generally poorly at the detection task, they show no significant biases on the studied attributes.
Abstract (translated)
文本生成能力的迅速提升伴随着对机器生成文本检测兴趣的平行增长：即识别给定文本是否由模型生成还是由人书写的这一能力。尽管检测模型表现出色，但它们有可能产生显著的负面影响。我们探讨了英语机器生成文本检测系统中存在的潜在偏见。为此，我们收集了一组学生作文，并评估了16种不同的检测系统在这四个属性上的偏差：性别、种族/族裔、作为英语语言学习者（ELL）的状态以及经济状况。我们使用基于回归的模型来确定这些影响的重要性和效力，并进行了子群体分析。研究发现，虽然不同系统之间的偏见通常并不一致，但存在几个关键问题：多个模型倾向于将弱势群体归类为机器生成；作为ELL的学生作文更容易被分类为机器生成；经济条件较差学生的作品更不容易被分类为机器生成；而非白人ELL学生的作文则比其白人同学的作品更多地被错误分类为机器生成。最后，我们进行了人工标注，并发现虽然人类在检测任务上表现一般较低，但在研究的属性方面并未显示出显著偏见。
URL
https://arxiv.org/abs/2512.09292
