@proceedings {9999,
	title = {Impacts of the Use of Machine Learning on Work Design},
	year = {2020},
	month = {11/2020},
	publisher = {ACM},
	address = {Virtual Event, NSW, Australia},
	abstract = {<p>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) to automate information tasks, we draw on a simple framework for identifying the impacts of an automated system on a task that suggests 3 patterns for the use of ML{\textemdash}decision support, blended decision making and complete automation. In this paper, we extend this framework by considering how automation of one task might have implications for interdependent tasks and how automation applies to coordination mechanisms.</p>},
	keywords = {artificial intelligence, automation, Coordination, machine learning, work design},
	isbn = {978-1-4503-8054-6/20/11},
	doi = {10.1145/3406499.3415070},
	attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/Impacts_of_ML_for_HAI_2020.pdf},
	author = {Kevin Crowston and Bolici, Francesco}
}
@proceedings {9999,
	title = {Impacts of machine learning on work},
	year = {2019},
	address = {Wailea, HI},
	abstract = {<p>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.</p>},
	keywords = {artificial intelligence, automation, machine learning, work design},
	doi = {10.24251/HICSS.2019.719},
	url = { http://hdl.handle.net/10125/60031},
	attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/Impacts_of_machine_learning_on_work__revision_.pdf},
	author = {Kevin Crowston and Bolici, Francesco}
}
