In this paper, we focus on limitations in the use of the Shapley value within the field of eXplainable AI (XAI) through the lens of the axiomatic analysis and its implications in the realm of machine learning. As an alternative to the Shapley value, we analyse the properties of the lex-cel, a social ranking solution introduced inthe recent literature at the intersection between coalitional games and social choice theory, showing that axioms characterizing thelex-cel, under certain circumstances, are more suitable for ranking features in machine learning models, compared to those satisfied bythe Shapley value. Via experiments conducted on public datasets, we also show that the lex-cel outperforms a commonly employed feature selection algorithm based on the Shapley value, in particular with respect to the capacity of selecting less redundant features