AI评分系统对英语学习者存在偏见，数据量不足时尤其明显
Artificial Intelligence Bias on English Language Learners in Automatic Scoring
- 用四种数据集微调BERT，对比不同样本下评分偏差
- 3万和1千样本时无明显偏差，200样本时问题突出
- 提醒教育AI应用需关注小样本群体的数据公平性
本研究调查了自动评分系统在中学科学评估中对英语学习者（ELLs）的潜在评分偏见与差异。重点分析了包含未平衡英语学习者数据的训练集如何导致评分偏差。我们使用四个数据集对BERT进行微调：(1) 英语学习者回答，(2) 非英语学习者回答，(3) 反映真实比例的非平衡混合数据集，(4) 平衡的混合数据集。研究分析了21个评估题项：10个题项含约3万条英语学习者回答，5个题项含约1千条，6个题项含约200条。通过弗里德曼检验比较评分准确率（Acc），并计算英语学习者与非英语学习者之间的平均分差（MSG），再比较人工与AI模型产生的MSG差异以识别评分差异。结果表明，当英语学习者数据量足够大（3万和1千）时，未发现显著的AI偏见与扭曲差异；但在样本量有限（200）时，存在潜在风险。
原文摘要 · Abstract (English)
This study investigated potential scoring biases and disparities toward English Language Learners (ELLs) when using automatic scoring systems for middle school students' written responses to science assessments. We specifically focus on examining how unbalanced training data with ELLs contributes to scoring bias and disparities. We fine-tuned BERT with four datasets: responses from (1) ELLs, (2) non-ELLs, (3) a mixed dataset reflecting the real-world proportion of ELLs and non-ELLs (unbalanced), and (4) a balanced mixed dataset with equal representation of both groups. The study analyzed 21 assessment items: 10 items with about 30,000 ELL responses, five items with about 1,000 ELL responses, and six items with about 200 ELL responses. Scoring accuracy (Acc) was calculated and compared to identify bias using Friedman tests. We measured the Mean Score Gaps (MSGs) between ELLs and non-ELLs and then calculated the differences in MSGs generated through both the human and AI models to identify the scoring disparities. We found that no AI bias and distorted disparities between ELLs and non-ELLs were found when the training dataset was large enough (ELL = 30,000 and ELL = 1,000), but concerns could exist if the sample size is limited (ELL = 200).
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