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CHI-ANNOUNCEMENTS Home

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CHI-ANNOUNCEMENTS  December 2015, Week 1

CHI-ANNOUNCEMENTS December 2015, Week 1

Subject:

CFP: CHI 2016 workshop on Human-Centred Machine Learning

From:

Rebecca Fiebrink <[log in to unmask]>

Reply-To:

Rebecca Fiebrink <[log in to unmask]>

Date:

Mon, 7 Dec 2015 08:53:17 -0500

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (60 lines)

***************************************************************************
CALL FOR PAPERS: CHI 2016 Workshop on Human-Centred Machine Learning
A one-day workshop at CHI 2016, San Jose, CA, USA (http://chi2016.acm.org/wp/)
7th or 8th May 2016
http://hcml2016.goldsmithsdigital.com/

***************************************************************************

IMPORTANT DATES
11 December 2015: *Early* submission date (optional; for authors dependent on 2015 budgets); notification 21 December
8 January 2016: Deadline for normal paper submission
15 January 2016: Acceptance notification for normal paper submissions
5 February 2016: Deadline for final copy of accepted papers
7 or 8 May 2016: Workshop date
9-12 May 2016: Main CHI Conference (Workshop participants must register for at least 1 day of the main conference)


THE WORKSHOP
Machine learning is one of the most important and successful techniques in contemporary computer science, with applications ranging from from medical research to the arts, as well as considerable recent interest in its use for interaction design. It is often conceived in a very impersonal way, with algorithms working autonomously on passively collected data. However, this viewpoint hides considerable human work of tuning the algorithms, gathering the data, and even deciding what should be modeled in the first place. 

Examining machine learning from a human-centered perspective includes explicitly recognising this human work, as well as reframing machine learning workflows based on situated human working practices, and exploring the co-adaptation of humans and systems. A human-centered understanding of machine learning in human context can lead not only to more usable machine learning tools, but to new ways of framing learning computationally. This workshop will bring together researchers to discuss these issues and suggest future research questions aimed at creating a human-centered approach to machine learning. We will also invite participants to help us in establishing and maintaining a community around human-centred machine learning, including running a follow-up workshop at a machine learning conference such as NIPS.


HOW TO PARTICIPATE
We invite participants to submit 2-6 page position papers in the CHI Extended Abstracts format (https://chi2016.acm.org/wp/guide-to-submission-formats/) to be submitted via our EasyChair  electronic submission site (https://easychair.org/conferences/?conf=hcml2016).

Topics may include (but are not limited to):
* the role of humans in current machine learning
* usability challenges of machine learning
* new machine learning methodologies based on human-centered research
* new human-centered machine learning systems
* evaluation methods for human-centered machine learning
* human-centered machine learning in domains such as arts, science and social science

Papers will be reviewed by committee members and accepted authors will present at the workshop. At least one author of each accepted position paper must attend the workshop and must register for both the workshop and for at least one day of the conference. Presentations will be in a panel format to encourage discussion: 3-4 participants will present together as part of a thematic panel. Each panel participant will give a short (10 minute) presentation of their work followed by a joint discussion.

CONTACT
If you have any questions, please contact us at [log in to unmask]
Workshop website: http://hcml2016.goldsmithsdigital.com/

ORGANISERS
Marco Gillies, Rebecca Fiebrink, Atau Tanaka, Jérémie Garcia: Goldsmiths, London, UK
Frédéric Bevilacqua: IRCAM, Paris, France
Alexis Heloir, Fabrizio Nunnari DFKI, Saarbrücken, Germany
Wendy Mackay: INRIA, Paris, France
Saleema Amershi, Bongshin Lee: Microsoft Research, Redmond, WA, USA
Baptiste Caramiaux, McGill University, Canada
Nicolas d’Alessandro, Joëlle Tilmanne: University of Mons, Belgium
Todd Kulesza, Oregon State University, USA

    ---------------------------------------------------------------
    For news of CHI books, courses & software, join CHI-RESOURCES
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    To unsubscribe from CHI-ANNOUNCEMENTS send an email to
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