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

arXiv: https://arxiv.org/abs/2109.07908 · HTML: https://mlsanity.com/paper/2109.07908/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 64 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Algorithmic Fairness in Education](https://mlsanity.com/paper/2007.05443/) (2020)
- [Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data](https://mlsanity.com/paper/1809.02519/) (2018)
- [Fair Active Learning](https://mlsanity.com/paper/2001.01796/) (2020)
- [Ethics-Based Auditing to Develop Trustworthy AI](https://mlsanity.com/paper/2105.00002/) (2021)
- [Assessing and Remedying Coverage for a Given Dataset](https://mlsanity.com/paper/1810.06742/) (2018)
- [OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning](https://mlsanity.com/paper/2103.09055/) (2021)
- [The Impact of Data Preparation on the Fairness of Software Systems](https://mlsanity.com/paper/1910.02321/) (2019)
- [Avoiding Resentment Via Monotonic Fairness](https://mlsanity.com/paper/1909.01251/) (2019)
- [Under the Radar -- Auditing Fairness in ML for Humanitarian Mapping](https://mlsanity.com/paper/2108.02137/) (2021)

## Abstract (arXiv)

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.