Impacts of machine learning on work

Kevin Crowston, Francesco Bolici

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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.
Original languageEnglish (US)
Title of host publicationProceedings of the 52nd Hawaii International Conference on System Sciences
Place of PublicationWailea, HI
Volume52
StatePublished - 2019

Keywords

  • artificial intelligence, automation, machine learning, work design

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    Crowston, K., & Bolici, F. (2019). Impacts of machine learning on work. In Proceedings of the 52nd Hawaii International Conference on System Sciences (Vol. 52). http://hdl.handle.net/10125/60031