分析高校招生AI模型中的性别、种族和家庭首代大学生偏见，揭示测试成绩取消对录取公平性的影响。
Bias Analysis of AI Models for Undergraduate Student Admissions
- 基于六年招生数据构建预测模型，评估变量重要性与政策变化影响。
- 发现取消标准化考试后，少数群体录取率仍存在系统性差异。
- 揭示偏见具有持续性，适合关注算法公平性的研究者参考。
偏差检测与缓解是机器学习领域的活跃研究方向。本文扩展了作者先前的研究，对人工智能预测模型中的偏差进行了更严谨、全面的分析。利用某大型城市研究型大学六年的招生数据，构建了用于判断学生是否被理学院直接录取的AI模型。在此期间，申请中提交标准化考试成绩变为可选，引发了关于考试成绩对录取决策影响的探讨。我们开发并分析了多个模型，以理解各变量在录取决策中的重要性，并评估取消考试成绩对录取学生人口结构的影响。针对性别、种族以及是否为家庭首代大学生三类敏感变量，系统检测并分析了模型可能携带的偏差。结果表明，所发现的偏差具有持续性。此外，我们在分析中引入多种公平性度量，讨论了其应用价值与局限性。
原文摘要 · Abstract (English)
Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of an application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his or her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。