The representation of concepts is a lively research activity in several artificial intelligence (AI) areas, such as knowledge representation, machine learning, and natural language processing. So far, many solutions have been proposed adopting different assumptions about the nature of concepts. Each of these solutions has been developed for capturing some specific features and for supporting some specific (artificial) cognitive operations. This paper provides a teleological explanation of the most widely shared approaches in AI to the representation of concepts. The paper aims at providing four main contributions: i) an overview of the mainstream philosophical theories of concepts; ii) a categorization of a wide range of AI solutions inspired by such theories of concepts; iii) the proposal of a method for a comprehensive explanation of the current approaches to concepts in AI based on a teleosemantic perspective; iv) an illustration of how the proposed explanation could constitute a contribution in the context of explainable AI.
