Impacts of Machine Learning on Work

TLDR

The rapid spread of automation technologies makes it urgent to understand how work is changing. The study proposes a framework for assessing the impacts of machine‑learning–driven automation on tasks and examines how automating one task can influence related tasks. The authors analyze popular press articles on ML to develop a framework and three patterns—decision support, blended decision making, and complete automation—illustrating how ML can automate information tasks. The study concludes that designers can choose among multiple ML automation patterns and that automating tasks does not equate to automating entire work.

Abstract

The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) that automate information tasks, we present a simple framework for identifying the impacts of an automated system on a task. From an analysis of popular press articles about ML, we develop 3 patterns for the use of ML—decision support, blended decision making and complete automation—with implications for the kinds of tasks and systems. We further consider how automation of one task might have implications for other interdependent tasks. Our main conclusion is that designers have a range of options for systems and that automation of tasks is not the same as automation of work.