We explore how people developing or using a system with a machine-learning (ML) component come to understand the capabilities and challenges of ML. We draw on the social construction of technology (SCOT) tradition to frame our analysis of interviews and discussion board posts involving designers and users of a ML-supported citizen-science crowdsourcing project named Gravity Spy. We extend SCOT by anchoring our investigation in the different uses of the technology. We find that the type of understandings achieved by groups having less interaction with the technology is shaped more by outside influences and less by the specifics of the system and its role in the project. This initial understanding of how different participants understand and engage with ML points to challenges that need to be overcome to help users of a system deal with the opaque position that ML often holds in a work system.