Should College Dropout Prediction Models Include Protected Attributes?
Should College Dropout Prediction Models Include Protected Attributes?
复制标题
大学辍学预测模型是否应该包含受保护的属性？
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
René F. Kizilcec
中科院分区：
文献类型：
--
作者：
Renzhe Yu;Hansol Lee;René F. Kizilcec
及早识别大学辍学生可以为提高学生的成功和学校的效率提供巨大的价值，预测性分析越来越多地用于这一目的。然而，在这些预测模型中纳入受保护的属性是否会歧视未被充分代表的学生群体并加剧现有的不平等，这引发了伦理方面的担忧。我们在一所美国大型研究型大学的背景下研究了这个问题，该大学既有寄宿学生，也有完全在线的学位寻求者。基于整个学生群体多年来(N=93,457)的综合机构记录，我们建立了机器学习模型来预测学生在一学年后的辍学，并比较了在有或没有四个受保护属性(性别、URM、第一代学生和高经济需求)的情况下模型预测的整体性能和公平性。我们发现，包含受保护属性并不影响整体预测性能，它仅略微提高了预测的算法公平性。这些发现表明，包括受保护的属性更可取。我们就如何在当地环境中评估纳入受保护属性的影响提供指导，在当地，机构利益相关者寻求利用预测性分析来支持学生的成功。
Early identification of college dropouts can provide tremendous value for improving student success and institutional effectiveness, and predictive analytics are increasingly used for this purpose. However, ethical concerns have emerged about whether including protected attributes in these prediction models discriminates against underrepresented student groups and exacerbates existing inequities. We examine this issue in the context of a large U.S. research university with both residential and fully online degree-seeking students. Based on comprehensive institutional records for the entire student population across multiple years (N = 93,457), we build machine learning models to predict student dropout after one academic year of study and compare the overall performance and fairness of model predictions with or without four protected attributes (gender, URM, first-generation student, and high financial need). We find that including protected attributes does not impact the overall prediction performance and it only marginally improves the algorithmic fairness of predictions. These findings suggest that including protected attributes is preferable. We offer guidance on how to evaluate the impact of including protected attributes in a local context, where institutional stakeholders seek to leverage predictive analytics to support student success.