Hybrid human-ML systems are increasingly in charge of consequential decisions in a wide range of domains. Drawing upon prior literature in human psychology, machine learning, and human-computer interaction, we introduce a taxonomy characterizing a wide variety of criteria across which human and machine decision-making differ. Our taxonomy offers a critical piece of insight for future work in human-ML collaborative decision-making:the mechanism by which we combine human-ML judgments should be informed by the underlying causes of their diverging decisions.