Evaluating the Promise of Human-Algorithm Collaborations in Everyday Work Practices

Abstract

Human-algorithm interaction is a growing phenomenon of interest as the use of machine learning (ML) capabilities in everyday technologies becomes more commonplace. In the workplace, such developments raise questions about how people not only make sense of algorithmic actions, but also figure out ways to collaborate with tools and systems that integrate algorithmic outputs. We draw on a field study of IT infrastructure design and report on the experiences of highly-skilled IT architects with the natural language processing (NLP) capabilities in an intelligent system under development to support their solution design work. While architects were supportive of the potential of NLP to enhance their solutioning work, they faced challenges in integrating such capabilities into their existing collaborative work practices. We discuss how these findings add nuance and complexity to discourse around the future of work.