Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education

TL;DR

This paper examines how different imputation techniques affect the fairness of predictive models for student success in higher education, highlighting the importance of addressing societal inequalities to reduce bias.

Contribution

It provides a comprehensive analysis of the impact of imputation methods on fairness and performance in predictive analytics for higher education, using real large-scale data.

Findings

Imputation can introduce bias if test data reflects historical societal disparities.

Equalizing societal disparities in data reduces bias introduced by imputation.

Prospective evaluation offers a less biased estimate of model fairness and performance.

Abstract

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.