  Measures of algorithmic fairness often do not account for human perceptions
of fairness that can substantially vary between different sociodemographics and
stakeholders. The FairCeptron framework is an approach for studying perceptions
of fairness in algorithmic decision making such as in ranking or
classification. It supports (i) studying human perceptions of fairness and (ii)
comparing these human perceptions with measures of algorithmic fairness. The
framework includes fairness scenario generation, fairness perception
elicitation and fairness perception analysis. We demonstrate the FairCeptron
framework by applying it to a hypothetical university admission context where
we collect human perceptions of fairness in the presence of minorities. An
implementation of the FairCeptron framework is openly available, and it can
easily be adapted to study perceptions of algorithmic fairness in other
application contexts. We hope our work paves the way towards elevating the role
of studies of human fairness perceptions in the process of designing
algorithmic decision making systems.
