论文标题
在学术绩效预测中，均等赔率作为公平度量
Towards Equalised Odds as Fairness Metric in Academic Performance Prediction
论文作者
论文摘要
公平感知机器学习的文献知道了很多不同的公平概念。然而，众所周知，不可能满足所有人的满足，因为某些概念相互矛盾。在本文中，我们仔细研究了学术绩效预测（APP）系统，并尝试提出最适合这项任务的公平性。为此，我们扫描了最近的文献提出了有关使用哪些公平概念并将这些准则应用于应用程序的准则。我们的发现表明，基于App的Wysiwyg Worldview以及对人群的潜在长期改善，均等的赔率是最适合应用程序的概念。
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.
