Is There Fairness in AI?

Abstract

As predictive artificial intelligence (AI) technologies increasingly steer workplace decisions, debates around fairness have intensified. Existing research often approaches fairness either as a set of universal principles supported or undermined by algorithms, or as a product of social interpretations, thereby providing either technologically deterministic or purely social accounts. Drawing on an ethnographic study of a human resources (HR) department of a large international company that introduced AI in hiring, this study offers an alternative view that shifts focus to how fairness emerges through the ways people define, embed, and perform values with algorithms. Taking a sociomaterial perspective, we find that the introduction and use of AI resulted in crowding out expert practices of performing fairness, favouring instead the version performed by HR. Our process model explains this outcome by the growing symbiosis between HR's professional mandate for fairness and AI procedures, where each legitimizes, shapes, and protects the other over time. This study thus shows that fairness is not pre-given but constantly redefined and enacted through evolving associations between professional mandates and AI technologies.