We argue that, as well as more obvious concerns about the downstream effects of ML-based decision-making, there can be moral grounds for the criticism of these predictions themselves. We introduce and defend a theory of predictive justice, according to which differential model performance for systematically disadvantaged groups can be grounds for moral criticism of the model, independently of its downstream effects. As well as helping resolve some urgent disputes around algorithmic fairness, this theory points the way to a novel dimension of epistemic ethics, related to the recently discussed category of doxastic wrong.