Perceptions of Machine Learning: The Genie in the Bottle

Publication Type:

Conference Proceedings




Machine learning (ML) constitute algorithmic phenomenon with some distinctive characteristics (e.g., being trained, probabilistic). Our understanding of such systems is limited when it comes to how these unique characteristics play out in organizational settings and what challenges di fferent groups of users will face in working with them. We explore how people are developing or using an ML system come to understand its capabilities and challenges. We draw on the social construction of technology tradition to frame our analysis of interviews and discussion board posts involving designers and users of an ML supported citizen-science crowdsourcing project named Gravity Spy. Our ndings reveal some of the challenges facing di erent relevant social groups. We nd that the type of understandings achieved by groups having less interaction with the technology is shaped more by outside influences and less by the species of the system and its role in the project. Notable, some users mistake human input for ML input. This initial understanding of how di erent participants understand and engage with ML point to challenges that need to be overcome to help participants deal with the opaque position ML often held in a work system.

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