"It's the most fair thing to do but it doesn't make any sense": Perceptions of Mathematical Fairness Notions by Hiring Professionals

Abstract

We explore the alignment of organizational representatives involved in hiring processes with five different, commonly proposed fairness notions. In a qualitative study with 17 organizational professionals, for each notion, we investigate their perception of understandability, fairness, potential to increase diversity, and practical applicability in the context of early candidate selection in hiring. In this, we do not explicitly frame our questions as questions of algorithmic fairness, but rather relate them to current human hiring practice. As our findings show, while many notions are well understood, fairness, potential to increase diversity and practical applicability are rated differently, illustrating the importance of understanding the application domain and its nuances, and calling for more interdisciplinary and human-centered research into the perception of mathematical fairness notions.