审计高等教育预测分析公平性的归因效应
Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education
摘要：
学院和大学以各种方式使用预测分析来提高学生的成功率。尽管预测分析具有潜力，但在高等教育中采用它们存在两个主要障碍：（a）部署缺乏民主化，（b）可能加剧不平等。教育研究人员和政策制定者在实践中部署预测建模时遇到了许多挑战。这些挑战存在于建模的不同步骤中，包括数据准备、模型开发和评估。然而，如果执行不当，这些步骤中的每一个都可能给系统引入额外的偏倚。大多数大规模和具有全国代表性的教育数据集都受到研究参与者大量不完整答复的困扰。虽然许多与教育相关的研究解决了缺失数据的挑战，但在实践中，处理缺失值对预测结果公平性的影响知之甚少。在本文中，我们首先评估了大学生成功预测建模结果的差异，然后使用一组常用的指标来研究插补技术对模型性能和公平性的影响。我们进行前瞻性评估，以提供比历史数据评估更少的未来绩效和公平性偏差估计。我们对一个真实的大规模教育数据集的全面分析揭示了建模差异的关键见解，以及插补技术如何影响不同测试场景下学生成功预测结果的公平性。我们的研究结果表明，如果测试集遵循历史分布，插补会引入偏倚。然而，如果社会中的不公正得到解决，因此即将到来的一批观察结果是平等的，模型将减少偏见。
英文摘要：
Colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potential for predictive analytics, two major barriers exist to their adoption in higher education: (a) the lack of democratization in deployment, and (b) the potential to exacerbate inequalities. Education researchers and policymakers encounter numerous challenges in deploying predictive modeling in practice. These challenges present in different steps of modeling including data preparation, model development, and evaluation. Nevertheless, each of these steps can introduce additional bias to the system if not appropriately performed. Most large-scale and nationally representative education data sets suffer from a significant number of incomplete responses from the research participants. While many education-related studies addressed the challenges of missing data, little is known about the impact of handling missing values on the fairness of predictive outcomes in practice. In this paper, we set out to first assess the disparities in predictive modeling outcomes for college-student success, then investigate the impact of imputation techniques on the model performance and fairness using a commonly used set of metrics. We conduct a prospective evaluation to provide a less biased estimation of future performance and fairness than an evaluation of historical data. Our comprehensive analysis of a real large-scale education dataset reveals key insights on modeling disparities and how imputation techniques impact the fairness of the student-success predictive outcome under different testing scenarios. Our results indicate that imputation introduces bias if the testing set follows the historical distribution. However, if the injustice in society is addressed and consequently the upcoming batch of observations is equalized, the model would be less biased.
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