Laumer S (2026)
Publication Type: Conference contribution, Conference Contribution
Publication year: 2026
Original Authors: Sven Laumer
Pages Range: 35-41
Artifical intelligence (AI) enabled information systems (IS) are widely introduced to enhance fairness by improving accuracy and consistency, yet they often provoke intensified debates about fairness with divergent fairness perceptions. This study examines this paradox through a qualitative case study of the Video Assistant Referee (VAR) in professional football. Drawing on work system and organizational justice theory, we analyze fairness perceptions across interconnected work systems, including referees, governing bodies, players, coaches, and fans. Based on an inductive qualitative content analysis of public stakeholder statements, we develop a theoretical statement that conceptualizes AI fairness as a relational and socio-technical phenomenon emerging from interactions between work systems rather than from algorithmic properties or interactions between focal social and technological characteristics alone. This statement is elaborated through three propositions explaining (1) how fairness is evaluated within a focal work system, (2) how divergent fairness perceptions arise through interactions between work systems with different justice logics, and (3) how these divergences translate into distinct and sometimes conflicting design requirements for AI-enabled IS.
APA:
Laumer, S. (2026). Artificial Intelligence and Divergent Fairness Perceptions: Implications from the Video Assistant Referee in Football. In Association for Computing Machinery (Eds.), Proceedings of the SIGMIS-CPR '26: Proceedings of the 63rd ACM Conference on Computers and People Research (pp. 35-41). Flagstaff ,AZ, US.
MLA:
Laumer, Sven. "Artificial Intelligence and Divergent Fairness Perceptions: Implications from the Video Assistant Referee in Football." Proceedings of the SIGMIS-CPR '26: Proceedings of the 63rd ACM Conference on Computers and People Research, Flagstaff ,AZ Ed. Association for Computing Machinery, 2026. 35-41.
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