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Paradoxical Tensions Related to AI-Powered Evaluation Systems in Competitive Sports

Author

Listed:
  • Elena Mazurova

    (Aalto University)

  • Willem Standaert

    (HEC Liège – Management School of the University of Liège
    Ghent University)

  • Esko Penttinen

    (Aalto University)

  • Felix Ter Chian Tan

    (School of Information Systems and Technology Management at UNSW Business School)

Abstract

Judging in competitive sports is prone to errors arising from the inherent limitations to humans’ cognitive and sensorial capabilities and from various potential sources of bias that influence judges. Artistic gymnastics offers a case in point: given the complexity of scoring and the ever-increasing speed of athletes’ performance, systems powered by artificial intelligence (AI) seem to promise benefits for the judging process and its outcomes. To characterize today’s human judging process for artistic gymnastics and examine contrasts against an AI-powered system currently being introduced in this context, an in-depth case study analyzed interview data from various stakeholder groups (judges, gymnasts, coaches, federations, technology providers, and fans). This exploratory study unearthed several paradoxical tensions accompanying AI-based evaluations in this setting. The paper identifies and illustrates tensions of this nature related to AI-powered systems’ accuracy, objectivity, explainability, relationship with artistry, interaction with humans, and consistency.

Suggested Citation

  • Elena Mazurova & Willem Standaert & Esko Penttinen & Felix Ter Chian Tan, 2022. "Paradoxical Tensions Related to AI-Powered Evaluation Systems in Competitive Sports," Information Systems Frontiers, Springer, vol. 24(3), pages 897-922, June.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:3:d:10.1007_s10796-021-10215-8
    DOI: 10.1007/s10796-021-10215-8
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    References listed on IDEAS

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    1. Daniel Robey & Marie-Claude Boudreau, 1999. "Accounting for the Contradictory Organizational Consequences of Information Technology: Theoretical Directions and Methodological Implications," Information Systems Research, INFORMS, vol. 10(2), pages 167-185, June.
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    3. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
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    1. Babak Abedin & Christian Meske & Iris Junglas & Fethi Rabhi & Hamid R. Motahari-Nezhad, 2022. "Designing and Managing Human-AI Interactions," Information Systems Frontiers, Springer, vol. 24(3), pages 691-697, June.

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