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.

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

Harandi, M., Crowston, K., Jackson, C., & Østerlund, C.. (2020). The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning. In Hawai'i International Conference on System Science. https://doi.org/10.24251/HICSS.2020.719

Factors Influencing Approval of Wikipedia Bots

Dalgali, A., & Crowston, K.. (2020). Factors Influencing Approval of Wikipedia Bots. In Hawai'i International Conference on System Science. https://doi.org/10.24251/HICSS.2020.018

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:
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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 (note that you must be logged in to the site to view some of these):

Artificial Intelligence and Work: AAAI 2019 Fall Symposium

A two and one-half day symposium was 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 symposium schedule and papers are available (NB. you must be logged in to the website to see the papers).

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.

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Recent tweets

  • From the New York Times (a Guest Essay): If You Don’t Trust A.I. Yet, You’re Not Wrong: https://t.co/KxMkbJJtQh — 2 weeks 5 days ago
  • From the New Yorker: Why Computers Won’t Make Themselves Smarter: https://t.co/BnqHmnxNNC — 3 weeks 2 days ago
  • From MIT Technology Review: “We’ll never have true AI without first understanding the brain”: https://t.co/dgXz5FCP7p — 3 weeks 3 days ago
  • From the NY Times: A.I. Is Not What You Think: https://t.co/c9N09xEsti — 3 weeks 5 days ago
  • From The New Yorker: What Data Can’t Do: https://t.co/2RzK09k5M1 — 1 month 18 hours ago
  • From Venture Beat: AI Weekly: AI research still has a reproducibility problem: https://t.co/ubyIkOlvWG — 1 month 2 days ago
  • From AI Magazine: Looking Back, Looking Ahead: Humans, Ethics, and AI: https://t.co/1LZytSgEBU — 1 month 3 days ago
  • From Medical Device + Diagnostic Industry (MD+DI): Ethical Artificial Intelligence: Potential Standards for Medical… https://t.co/XLT5K8xnCx — 1 month 1 week ago
  • From Communications of the ACM: Biases in AI Systems: https://t.co/wPy5i1u5GH — 1 month 1 week ago
  • From the New York Times: Using A.I. to Find Bias in A.I.: https://t.co/ARoFyNGuYS — 1 month 1 week ago

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