Why ‘machine Learning’ is a Misnomer

Abstract

This paper problematises the notion of ‘machine learning’ (ML) challenging its applicability to contemporary algorithms. By drawing on theoretical insights from Learning Sciences, praxeological, and social-cognitive perspectives of learning and agency, this paper highlights discrepancies between human learning (learning proper) and the operations of machine algorithms misnomered as ‘learning’. Through a comparative analysis, the paper reveals fundamental misalignments between ‘machine learning’ and learning proper. Furthermore, the paper scrutinises the implications of co-opting uniquely human nomenclature to describe mechanistic phenomena. By contributing a nuanced critique of ML/AI discourse, the paper aims to foster a clearer understanding of the nature of ML and AI, promoting a necessary reassessment of prevailing narratives surrounding these technologies.

Keywords: machine learning, artificial intelligence, human learning, agency