[DigitalToday reporter Yoonseo Lee] The use of artificial intelligence (AI) to discover football prospects is increasing, but critics say it could repeat existing bias and inequality rather than accurately identify future 'star players'.

On June 11 local time, online outlet Gigazine reported that Lia Mounthys (리아 몬시스), a sports science researcher at Malmö University in Sweden, said AI used in football scouting can help decision-making but has limits in objectively assessing talent itself.

Elite football academies and clubs have recently been collecting player data using GPS trackers, automated video analysis tools and AI platforms. Clubs are eager to adopt such technologies because the earlier they find prospects, the higher the profits they can expect.

The problem is that AI works by finding patterns among successful players based on past data. If traditional scouting has relatively favored physically outstanding players or those from certain socioeconomic backgrounds, AI could repeat the same tendency.

Early discovery cases have drawn attention in football. Spanish player Lamine Yamal was selected for FC Barcelona's youth setup at age 6 and made his first-team debut at 15 years, 9 months and 16 days. By contrast, Alex Morgan only began playing football in earnest as a teenager, and Luca Toni reached the top level in his early 20s. Because players develop physically, mentally and socially at different speeds, it is difficult to predict long-term potential based only on information from early childhood.

Concerns have also been raised that AI may not sufficiently reflect such differences. Mounthys said, "A data-driven approach can help decisions, but it cannot remove subjectivity in the end." She said people ultimately decide what data to collect, how to analyse it and what to regard as talent. Another limitation is that even with abundant quantified records, it is difficult to fully capture a player's experience, interactions and match context.

This structure also intersects with existing inequality in football. The path to becoming an elite player has been influenced by social, economic and cultural conditions, and if AI learns from such data it could worsen inequality. Even if players who mature physically earlier than their peers have an advantage, that does not mean long-term potential. Late bloomers such as Ian Wright and Luca Toni may not be recognised in such a system.

There is also a burden in player management. If young players become aware that their data is continually collected and analysed by AI, psychological pressure could increase as well as performance demands. Research found that intensified surveillance created pressure from being constantly evaluated not only for players but also for coaches and staff.

The use of AI itself was not rejected. Clubs can analyse vast amounts of information that people find hard to process, spot patterns that are easy to miss, and examine players without regional constraints. This has led to criticism that the key issue is how the technology is used, not whether it is introduced. Mounthys said, "The task ahead is not introducing new technology itself but reviewing how it is used," adding that clubs should recognise the limits of data and invest in education and expertise.

The discussion shows that even if AI can improve the efficiency of identifying players, training data and evaluation criteria do not automatically become fair. In sport, how to interpret data and combine it with human judgment is emerging as a more important task than the adoption of technology itself.