Finding Algorithmic Fairness: An Analysis of how the Literature Constructs Algorithmic Fairness from Different Stakeholder Perspectives

Authors

Stefanie Knippschild, University of Sydney Business SchoolFollow
Sebastian Boell, University of Sydney Business SchoolFollow
Kai Riemer, University of Sydney Business SchoolFollow
Sandra Peter, University of Sydney Business SchoolFollow

Abstract

Algorithms are increasingly used by organisations to make decisions or recommendations about their operations. This can include operations, such as credit lending or recruitment. To date, algorithmic fairness has been discussed from multiple different lenses in the literature. However, Information Systems understand algorithmic fairness within a socio-technical system involving different stakeholders. We thus need to understand how existing research socially constructs algorithmic fairness from different stakeholder perspectives in organisational contexts. Our literature review outlines what stakeholders are discussed and how they construct algorithmic fairness. We show that different aspects of algorithmic fairness are considered by different stakeholders. We also show that employees, as decision-subjects in an algorithmic decision-making system, are predominantly used as study objects to derive implications about algorithmic fairness for management and developers.

Recommended Citation

Knippschild, Stefanie; Boell, Sebastian; Riemer, Kai; and Peter, Sandra, "Finding Algorithmic Fairness: An Analysis of how the Literature Constructs Algorithmic Fairness from Different Stakeholder Perspectives" (2024). ACIS 2024 Proceedings. 142.
https://aisel.aisnet.org/acis2024/142