Towards Equalised Odds as Fairness Metric in Academic Performance Prediction
Abstract
The literature for fairness-aware machine learning knows a plethora of different fairness notions. It is however wellknown, that it is impossible to satisfy all of them, as certain notions contradict each other. In this paper, we take a closer look at academic performance prediction (APP) systems and try to distil which fairness notions suit this task most. For this, we scan recent literature proposing guidelines as to which fairness notion to use and apply these guidelines onto APP. Our findings suggest equalised odds as most suitable notion for APP, based on APP's WYSIWYG worldview as well as potential long-term improvements for the population.
Cite
@article{arxiv.2209.14670,
title = {Towards Equalised Odds as Fairness Metric in Academic Performance Prediction},
author = {Jannik Dunkelau and Manh Khoi Duong},
journal= {arXiv preprint arXiv:2209.14670},
year = {2022}
}
Comments
FATED'22: 2nd Workshop on Fairness, Accountability, and Transparency in Educational Data. July 2022. Durham, England