Key result
Artificial intelligence and big data predictive tools in youth talent identification are vulnerable to biases and should be approached with caution regarding ethical issues.
Highlights the ethical issues and potential biases of using artificial intelligence and big data analytics in youth sports talent identification.
Recent technological developments, such as automatic broadcasting systems and computer vision, are enabling cost-effective data gathering, bringing big data analytics to youth sports. In a recent insightful article, Bar-Eli et al. (2023) discussed among other issues the subjective and objective talent selection approaches, advocating for the use of big data analytics to harness the benefits of both methods. My aim in this commentary is to complement that big data predictive tools also were found to be vulnerable to biases that render their decisions unfair. Therefore, artificial intelligence in the domain of youth talent identification should be approached with caution and in-depth consideration of ethical issues needs to be undertaken before these predictive tools can be widely adopted.
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Elia Morgulev (2023) conducted an editorial in Youth sports talent identification. Artificial intelligence and big data predictive tools was evaluated. Artificial intelligence and big data predictive tools in youth talent identification are vulnerable to biases and should be approached with caution regarding ethical issues.
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