Authors:
Andria Procopiou
and
Andriani Piki
Affiliation:
School of Sciences, University of Central Lancashire Cyprus, Larnaca, Cyprus
Keyword(s):
Explainable Artificial Intelligence, Machine Learning, Deep Learning, Football Analytics, Injury Prediction, Rehabilitation, Football Tactical Analysis, Human Factors, Human-Centred AI.
Abstract:
Artificial intelligence (AI) has demonstrated tremendous progress in many domains, especially with the vast deployment of machine and deep learning. Recently, AI has been introduced to the sports domain including the football (soccer) industry with applications in injury prediction and tactical analysis. However, the fact remains that the more complex an AI model is, the less explainable it becomes. Its black-box nature makes it difficult for human operators to understand its results, interpret its decisions and ultimately trust the model itself. This problem is magnified when the decisions and results suggested by an AI model affect the functioning of complex and multi-layered systems and entities, with a football club being such an example. Explainable artificial intelligence (XAI) has emerged for making an AI model more explainable, understandable and interpretable, thus assisting the creation of human-centered AI models. This paper discusses how XAI could be applied in the footba
ll domain to benefit both the players and the club.
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