Abstract

In Philosophy and Technology 36, David Watson discusses the epistemological and metaphysical implications of unsupervised machine learning (ML) algorithms. Watson is sympathetic to the epistemological comparison of unsupervised clustering, abstraction and generative algorithms to human cognition and sceptical about ML’s mechanisms having ontological implications. His epistemological commitments are that we learn to identify “natural kinds through clustering algorithms”, “essential properties via abstraction algorithms”, and “unrealized possibilities via generative models” “or something very much like them.” The same issue contains a commentary on Watson’s paper in which Tom Sterkenburg fiercely opposes the epistemological claim that clustering algorithms can identify natural kinds. Sterkenburg argues there’s nothing about clustering arguments themselves that enables natural kind identification. What’s at stake in the Watson-Sterkenburg debate is whether it is possible for clustering algorithms to identify natural kinds and whether they underly human identification of natural kinds. My contribution to the Watson-Sterkenburg debate is twofold. First, I argue Sterkenburg’s criticism of Watson’s claim is too severe because it denies any clustering algorithm to identify any natural kind. Secondly, I argue Watson’s reply overestimates both clustering algorithms’ and humans’ access to natural kinds and his claim has to be restricted accordingly to ‘We identify kinds via clustering algorithms, or something very much like them’.