The philosophy of computer science has recently addressed the ontological question of what constitutes a physical computational artifact. Despite their different nuances, all the analyses proposed so far largely rest on the central notions of specification, implementation, and correctness. In this talk, I extend this debate to machine learning (ML) systems, showing that all the three mentioned concepts need to undergo a substantial revision when it comes to ML artifacts. A ML ontology should accommodate a new epistemological role played by specification, defined as the set of functional requirements the artifact must satisfy. In predictive contexts, in fact, specifications are discovered through the actual training process rather than fixed from the beginning, this having in turn deep consequences on the relevant notions of correctness and implementation involved. A revised framework should allow us to formulate new and systematic insights into the notions of correctness and miscomputation, but also fairness and bias, particularly relevant in many decision-making contexts.