Artificial Intelligence and Work: AAAI 2019 Fall Symposium

We seek participants for a two and one-half day symposium to be held 7–9 November 2019 in Washington, DC, USA, to discuss and plan how AI researchers will contribute to research on human work with artificial intelligence. The draft symposium schedule and papers are now available (NB. you must be logged in to the website to see the papers).

About the RCN

Members of the WAIM network are developing the understanding needed to jointly design both sides of the human-technology frontier in work settings and using intelligent machines. Join now by clicking .

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.

The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning

Harandi, M., K. Crowston, C. Jackson, and C. Østerlund. 2020. “The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning”. in Hawai'i International Conference on System Science, Hawai'i International Conference on System Science.

Factors Influencing Approval of Wikipedia Bots

Dalgali, A., and K. Crowston. 2020. “Factors Influencing Approval of Wikipedia Bots”. in Hawai'i International Conference on System Science, Hawai'i International Conference on System Science. Wailea, HI.

Situating “explainability”: Making sense of data-driven assemblages in organizational context

Submitted by chris-wolf on Mon, 2019-08-19 08:21
Type: Presentation
Author: Christine T. Wolf
Year of Publishing: 2019
Keywords:
Download

The rise of data-driven technologies in recent years has sparked renewed attention to questions of technological sensemaking and in particular the explainability or interpretability of such systems (a growing technical subfield commonly called “Explainable AI” or “XAI”). These approaches tend to focus on the technology itself (often at the level of model or predictive output) and fail to consider the broader context of use and the situated nature of technological sensemaking.

WAIM Convergence Conference: At the Boundary: Exploring Human-AI Futures in Context

The 2nd WAIM Convergence Conference on the theme At the Boundary: Exploring Human-AI Futures in Context was held 14-15 August 2019 at Syracuse University's on the upper east side of Manhattan.

Some key documents:

Work in the Age of Intelligent Machines: Key Dilemmas for the Information Community

The workshop is over, but you can read the call below and see the notes from the workshop on Google Docs here:

Call for papers: Organization Science special issue on Emerging Technologies and Organizing

Submitted by crowston on Thu, 2019-04-04 09:39

Special Issue Editors
Diane Bailey, Samer Faraj, Pamela Hinds, Georg von Krogh, and Paul Leonardi

Workshop report: Developing Skills to Work in the Age of Intelligent Machines (Pre-HICSS workshop)

Submitted by crowston on Sat, 2019-02-09 17:56

A workshop was held 1pm – 4pm, 8 January 2019 in Wailea, HI, before the annual Hawai'i International Conference on System Sciences. It was the 4th workshop to be sponsored by the WAIM RCN. The workshop was organized by Elaine Mosconi, Université de Sherbrooke, Kevin Crowston, Syracuse University and Jeffrey Nickerson, Stevens Institute of Technology. The topic was "Developing Skills to Work in the Age of Intelligent Machines", though the discussion ranged more broadly. There were about 50 attendees.

Pages

Recent tweets

  • From Vox: An AI learned to play hide-and-seek. The strategies it came up with on its own were astounding: https://t.co/r7ncCE5ZpF — 13 hours 45 min ago
  • From Nature: Why deep-learning AIs are so easy to fool: https://t.co/UTwhH6kBUD — 1 day 10 hours ago
  • From the New Yorker: The Hidden Costs of Automated Thinking: https://t.co/qB22CvHzbp — 2 days 11 hours ago
  • From SIOP: Enhancing Judgment: The Case for Human–Algorithm Collaboration by Jon C. Willford, Edison Electric Insti… https://t.co/K9jT7EzsEd — 2 weeks 6 days ago
  • From Forbes: Apple Reminds Us That Behind The AI Revolution Lies An Army Of Humans Seeing Our Data: https://t.co/Smo7crZdpF — 3 weeks 6 days ago
  • From NYTimes: A.I. Is Learning From Humans. Many Humans: https://t.co/eXtgXNuw0o — 4 weeks 1 day ago
  • From Inverse: Robots are conducting job interviews now: https://t.co/GI5GDvVSEj — 4 weeks 1 day ago
  • From the Atlantic: AI Is Coming for Your Favorite Menial Tasks: https://t.co/jau5FSe6Ca — 1 month 1 day ago
  • From: The New Yorker: The Last Robot-Proof Job in America? https://t.co/RUHJGT5DFN — 1 month 1 day ago
  • From the Japan Times: A bot on your office computer could be sizing you up: https://t.co/dHxRFObhma — 1 month 2 days ago

Pages

more