  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.

Machine Learning;Artificial Intelligence;Computers and Society