Machine Learning (ML) systems typically yield definitive outputs, even when the underlying probabilities do not justify a decision. This poses a significant challenge in medical applications, where patients rely on individualized diagnoses, treatments, and prognoses. A recent advancement in ML research addresses this issue by introducing so-called “abstention models,” which enable ML systems to provide neutral outputs. From the perspective of a philosopher who works on cognitive neutrality and the suspension of judgment in human agents, this is an interesting field to explore. In this talk, I will introduce my philosophical theory of cognitive neutrality, which promotes various ways of suspending judgment. Against this backdrop, I will explore different abstention models and look for similarities and differences between suspension of judgment in humans and abstention in ML systems. In particular, I will examine whether the distinctions outlined in my cognitive neutrality framework also manifest in different models of abstention.