# Evaluation of Fairness Trade-offs in Predicting Student Success

arXiv: https://arxiv.org/abs/2007.00088 · HTML: https://mlsanity.com/paper/2007.00088/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks](https://mlsanity.com/paper/2102.05975/) (2021)
- [Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance](https://mlsanity.com/paper/2311.03311/) (2023)
- [Beyond "Fairness:" Structural (In)justice Lenses on AI for Education](https://mlsanity.com/paper/2105.08847/) (2021)
- [Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity](https://mlsanity.com/paper/2305.00927/) (2023)
- [Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models](https://mlsanity.com/paper/2305.15342/) (2023)
- [The Ethics of AI in Education](https://mlsanity.com/paper/2406.11842/) (2024)
- [Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling](https://mlsanity.com/paper/2209.10335/) (2022)
- [A Human-Centered Review of Algorithms in Decision-Making in Higher Education](https://mlsanity.com/paper/2302.05839/) (2023)
- [Equity and Fairness of Bayesian Knowledge Tracing](https://mlsanity.com/paper/2205.02333/) (2022)
- [Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students](https://mlsanity.com/paper/2405.09821/) (2024)

## Abstract (arXiv)

Predictive models for identifying at-risk students early can help teaching staff direct resources to better support them, but there is a growing concern about the fairness of algorithmic systems in education. Predictive models may inadvertently introduce bias in who receives support and thereby exacerbate existing inequities. We examine this issue by building a predictive model of student success based on university administrative records. We find that the model exhibits gender and racial bias in two out of three fairness measures considered. We then apply post-hoc adjustments to improve model fairness to highlight trade-offs between the three fairness measures.