Hybrid intelligence in business networks

Ebel, P., Söllner, M., Leimeister, J. Marco, Crowston, K., & de Vreede, G. - J.. (2021). Hybrid intelligence in business networks. Electronic Markets. https://doi.org/10.1007/s12525-021-00481-4

Using Work System Theory, Facets of Work, and Dimensions of Smartness to Characterize Applications and Impacts of AI

Submitted by stevenalter on Tue, 2020-11-24 18:16
Type: Report
Author: Steven Alter
Year of Publishing: 2020
Keywords: Artificial Intelligence, Algorithm, Work System Theory, Facets of Work, Dimensions of Smartness
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This paper presents an approach for describing and characterizing al-gorithms that are discussed as though they embody artificial intelligence. After identifying key assumptions related to algorithms and summarizing work sys-tem theory (WST), this paper uses a hypothetical example to introduces aspects of WST and two additional ideas, facets of work and dimensions of smartness in devices and systems. Next, it applies those ideas to aspects of five AI-related examples presented by entrepreneurs and researchers at an MIT AI conference in July 2020.

JASIST Special Issue on “Artificial Intelligence and Work”

Submitted by crowston on Wed, 2020-10-28 21:38

For this special issue of the Journal of the Association for Information Science and Technology (JASIST), we are calling for papers that advance the relationship between artificial Intelligence and work.

Looking for PhD students

Submitted by crowston on Fri, 2020-10-02 12:09

I am looking for PhD students interested in working on research about the future of work with intelligent machines, as well as the work on the Gravity Spy citizen science project. Please email if you're interested and want to talk more.

Impacts of the Use of Machine Learning on Work Design

Crowston, K., & Bolici, F.. (2020). Impacts of the Use of Machine Learning on Work Design. In 8th International Conference on Human-Agent Interaction. https://doi.org/10.1145/3406499.3415070

Artificial intelligence and the world of work, a co-constitutive relationship

Submitted by crowston on Mon, 2020-07-06 15:50

Check out this new JASIS&T paper based on the 2019 iConference workshop: . Thanks (and congrats) to Carsten, Mohammad, Matt, Karen and Christine!

Call for participation: Virtual paper development workshops for papers on work in the age of intelligent machines

Submitted by crowston on Fri, 2020-07-03 11:48

Our main activity over the past few years has been organizing workshops and an annual conference, but both are on hold due to the ongoing COVID crises. Indeed, with everything that’s going on, research may currently be lower-priority or even on pause for many people. Yet, as a research coordination network, we’d like to provide a small means of social support that may spur you on even in these challenging times. To this end, we are organizing a series of mini paper-development workshops, to be held virtually throughout the summer and perhaps beyond.

Lessons for Supporting Data Science from the Everyday Automation Experience of Spell-Checkers

Crowston, K. (2020). Lessons for Supporting Data Science from the Everyday Automation Experience of Spell-Checkers. In Automation Experience across Domains (AutomationXP20), CHI'20 Workshop, 26 April 2020, Virtual. Presented at the Automation Experience across Domains (AutomationXP20), CHI'20 Workshop, 26 April 2020, Virtual, Virtual workshop.

WAIM Convergence Conference 2020 postponed

Submitted by crowston on Mon, 2020-03-30 08:23

In light of the ongoing COVID-19 crisis, we have decided to postpone the 3rd annual WAIM Convergence Conference to northern hemisphere fall 2020. A new date and participation information will be posted soon.

CSCW Workshop: Better supporting workers in ML workplaces

This workshop is aimed at bringing together a multidisciplinary group to discuss Machine Learning and its application in the workplace as a practical, everyday work matter. It’s our hope this is a step toward helping us design better technology and user experiences to support the accomplishment of that work, while paying attention to workplace context. Despite advancement and investment in ML business applications, understanding workers in these work contexts have received little attention.

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