How Algorithms Change Occupational Expertise by Prompting Explicit Articulation and Testing of Experts’ Theories

Abstract

Prior research on algorithms in the workplace offers competing predictions. Some studies suggest that algorithms threaten knowledge workers’ expertise while other studies suggest that data scientists highly value knowledge workers’ “domain expertise” in developing algorithmic systems. Our findings from a 10-month ethnography at a retail tech company show how these competing predictions are in fact connected. We observed data scientists push their company’s fashion buyers to explicitly articulate the theories underlying their decisions and to use the rigorous analysis enabled by the algorithms to then reject or update those theories. These new cycles of explicit theory testing asked the buyers to help define and interpret the decisions. Yet they also asked the buyers to regularly reject and revise their theories, which was a new practice. We analyze the data scientists’ and buyers’ interactions that produced the theory testing capabilities and suggest that testing knowledge workers’ theories using algorithms is a new, co-produced expertise that both builds on and threatens their expertise. Our findings show that algorithms can render experts’ tacit knowledge visible for evaluation and testing—a process that simultaneously values them in the articulation of that knowledge while also threatening their expertise by potentially disproving its validity.

Keywords: algorithms, occupations, expertise, decision-making, ethnography