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.
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.
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.
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.
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.
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.