叠加劣势：在印度和美国STEM教育中对LLM生成解释中的交叉偏见进行审计
Compounding Disadvantage: Auditing Intersectional Bias in LLM-Generated Explanations Across Indian and American STEM Education
AI总结：
本文研究了大型语言模型在不同文化背景下对边缘化学生群体的系统性歧视，通过审计四个LLM模型发现，多重维度的边缘化导致成绩差距扩大，偏见在精英机构中持续存在。
摘要：
大型语言模型越来越多地被用于跨高收入和低收入国家的STEM教育中，以提供个性化教学和反馈。这些系统旨在根据学生需求调整内容，但其是否基于已展示的能力还是人口统计数据仍未经大规模测试。本文发现，LLM生成的STEM内容在两个文化背景下系统性地歧视边缘化学生群体，最特权和最边缘化群体之间的差距达到2.55个年级。通过使用合成的跨文化特定轮廓（包括印度教育的种姓、教学语言、学院层级和美国教育的种族、HBCU出席情况、学校类型），以及收入、性别和残疾状况，在排名和生成任务中对四个LLM（Qwen 2.5-32B-Instruct、GPT-4o、GPT-4o-mini、GPT-OSS 20B）进行了审计，采用FDR校正显著性测试和SHAP特征归因。收入在所有模型和上下文中产生显著影响，教学语言在印度情境中产生最大的单一影响，残疾状况触发更简单的解释。影响是非加性的：多个维度的边缘化导致差距大于任何单一维度预测的，偏见在精英机构中持续存在。偏见在所有四个架构中一致，并在模型选择中持续存在，因此在部署前进行交叉文化审计是结构化要求。
英文摘要：
Large language models are increasingly deployed in STEM education for personalized instruction and feedback across institutions in high- and low-income countries. These systems are designed to adapt content to student needs, but whether they adapt based on demonstrated ability or demographic signals remains untested at scale. Here we establish that LLM-generated STEM content systematically disadvantages marginalized student profiles across two cultural contexts, with the gap between the most privileged and most marginalized profiles reaching 2.55 grade levels. We audited four LLMs (Qwen 2.5-32B-Instruct, GPT-4o, GPT-4o-mini, GPT-OSS 20B) using synthetic profiles crossing dimensions specific to Indian education (caste, medium of instruction, college tier) and American education (race, HBCU attendance, school type), alongside income, gender, and disability, across ranking and generation tasks with FDR-corrected significance testing and SHAP feature attribution. Income produces significant effects across every model and context, medium of instruction drives the largest single effect in the Indian context, and disability status triggers simpler explanations. Effects compound non-additively: marginalization across multiple dimensions produces gaps larger than any single dimension predicts, and biases persist within elite institutions. Bias is consistent across all four architectures and persists through model selection, making intersectional, cross-cultural auditing a structural requirement before deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
