The World Cup & AI

2025-08-05 · Source: AI Policy Perspectives · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Public Policy & Governance · Depth: Intermediate, long

Summary

The Video Assistant Referee (VAR) system, introduced in the 2018 World Cup and now a feature in most elite national leagues, has become a significant source of fan frustration despite its aim to improve decision accuracy. While intended to correct "clear and obvious errors," VAR often creates new problems, particularly regarding the trade-off between decision accuracy and game speed. Checks, which average under a minute but can extend to eight minutes, disrupt the game's rhythm and temper goal celebrations. Furthermore, VAR's detailed scrutiny has led to an 11-fold expansion of rules like handball, attempting to codify subjective decisions that previously relied on referee discretion and common sense. This has resulted in increased complexity and perceived inconsistency, rather than improved clarity. The system often focuses on easily quantifiable aspects, like ball-to-hand contact, instead of the more crucial subjective intent, mirroring challenges in other domains where technology solves the wrong problems.

Key takeaway

For AI Ethicists and Policy Makers developing automated decision systems, you must critically evaluate the true problem being solved. Prioritize system speed and user involvement, recognizing that over-codifying subjective rules often creates more issues than it resolves. Your focus should be on incorporating tacit human knowledge through data-driven approaches, rather than attempting to automate every granular detail, to avoid unintended consequences and user frustration.

Key insights

Technological solutions to human problems often introduce unforeseen trade-offs, particularly between speed and accuracy or consistency and common sense.

Principles

• Speed can be an aspect of quality.
• Over-codifying subjective rules fails.
• Examples are more powerful than rules.

Method

A Foul Probability Index could use machine learning, trained on hundreds of thousands of video clips judged by professional referees and fans, to provide a percentage chance of a foul for new incidents.

In practice

• Prioritize speed in automated systems.
• Ensure user involvement in design.
• Incorporate tacit knowledge via data.

Topics

• Video Assistant Referee
• AI Ethics
• Automated Decision-Making
• Human-in-the-Loop Systems
• Machine Learning
• Sports Technology

Best for: AI Product Manager, Product Manager, AI Ethicist, Policy Maker, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Policy Perspectives.