“It’s the most fair thing to do, but it doesn’t make any sense”

Abstract

Mathematical fairness notions introduced in literature aim to make algorithmic decisions fair. However, their usage has been criticized in domains such as recidivism and lending for producing unfair decisions. Questions regarding fairness, which also have an important role in hiring are giving way to concerns about the increasing adoption of algorithmic decision-making systems in the field. However, there are no concrete studies linking mathematical notions to actual perceptions of fairness by people active in hiring and applicant selection.

We aim to explore the understanding and alignment of existing fairness notions by organizational representatives in the context of early candidate selection in hiring. Towards that, we interviewed 17 professionals from executive functions, talent acquisition, HR, I/O psychology, and diversity and inclusion operations in The Netherlands. By designing user-friendly illustrations and explanations in the context of early candidate selection in hiring, we explore their ratings and responses to six fairness notions on understandability, perception of fairness, perception of diversity, and applicability. Our qualitative investigation suggests that these fairness notions raise three concerns. One, they lack additional contexts such as a company's size or diversity goals. Two, they give rise to several ethical and practical concerns such as lack of trust in the data, disadvantaging minorities, or the selection of unqualified applicants. Lastly, they act only as a small step towards fairness in the large hiring pipeline. We conclude that a qualitative approach in collaboration between designers, practitioners, and policymakers is the key to refinement and contextualization of future technologically enabled fair hiring policies. Our participants' intrinsic motivation to engage with the topic of fairness strengthens our case.