[Air-L] CfP Critical Data Science workshop at ICWSM 2019 in Munich

Tue Mar 5 01:33:29 PST 2019

Dear Colleagues,

in addition to the cfp still open for the special issue of "Critical 
Data and Algorithm Studies" (see below!), we would like to extend the 
invitation to join us at the "Critical Data Science" workshop at ICWSM 
2019 in Munich.

With best regards

Katja Mayer

CALL FOR PAPERS
*Workshop on Critical Data Science*
at the 13th International AAAI Conference on Web and Social Media 
(ICWSM-2019),
Munich, Germany, June 11, 2019

https://projects.iq.harvard.edu/critical-data-science

*Submissions deadline: March 25, 2019*
*Acceptance notification: April 12, 2019*

We invite submissions to the Workshop on Critical Data Science, taking 
place on June 11, 2019 at the 13th International AAAI Conference on Web 
and Social Media (ICWSM-2019) in Munich, Germany.

-------------
The social world is far messier than technical training prepares one 
for. Among data scientists trained in fields like computer science and 
statistics are those experiencing a sense of vertigo: we start to 
realize both the ways in which modeling breaks down on human beings, 
requiring different notions of rigor, and the potentially negative 
social impacts of modeling, requiring responsible engagement and activity.

We define “critical data science” as our vision of the practice of 
working with and modeling data (the “data science”), combined with 
identifying and questioning the core assumptions commonly underlying 
that practice (the “critical”). The workshop seeks to combine cultures 
of critique with those of practice, bringing together data scientists 
and scholars from computer science and the social sciences around 
responsibly carrying out data science on social phenomena, and creating 
sustainable frameworks for interdisciplinary collaboration.

The workshop will involve short reflective presentations by 
participants, combined with a creative group-based activity to further 
support reflection of their own and neighboring scientific practices and 
to create opportunities for further cooperation. The workshop will 
conclude with a wrap-up for collecting resources and discussing future 
outcomes, and producing a draft compilation of best practices and a list 
of priorities for further engagement.

Submissions may either be non-archival 2-page statements of interest or 
motivation, or archival papers up to 4,000 words. Accepted archival 
papers will be published in Workshop Proceedings of the 13th 
International AAAI Conference on Web and Social Media 
<https://www.frontiersin.org/research-topics/9706>, a special issue of 
the journal Frontiers in Big Data. Open Access publishing costs will be 
waived for authors without institutional support for covering these fees.

Topics include:

  * What should be standards and practices both of methodological rigor,
    and of respect for subjects, when carrying out computational
    research on social systems?
  * What role can discussions of methods and instruments play in larger
    critiques of the limitations of data science?
  * What are points of fundamental disagreement or diverging
    orientations/priorities between disciplines?
  * What can we learn from the long tradition of critical scrutiny in
    statistics?
  * What combinations of experiences and/or readings has led data
    scientists to recognize, and perhaps even adopt, ‘non-technical’
    ways of framing the world? How do and can these ways of knowing
    interact with a modeling approach?
  * What philosophical commitments or normative orientations, if adopted
    by data scientists, would produce a principled data science? How can
    those be realized in interdisciplinary teams?
  * What might it look like to use modeling critically and reflexively,
    or to contextualize what we can or cannot know from modeling from
    within the modeling process?
  * What can we learn from works looking at the social impact of
    implemented model-based systems?
  * What sorts of practices, coalitions, and collaborations can include
    marginalized voices into data science rather than exclude them?
  * Beyond a space for critical reflection, what can be the positive
    project of a critical data science?
  * How can we design collaborations in critical data science?

See https://projects.iq.harvard.edu/critical-data-science for more 
information and submission instructions.

Contact: <criticaldatasci2019 at gmail.com 
<mailto:criticaldatasci2019 at gmail.com>>.

ORGANIZERS
*Momin M. Malik* <momin_malik at cyber.harvard.edu 
<mailto:momin_malik at cyber.harvard.edu>>, Berkman Klein Center for 
Internet & Society at Harvard University
*Katja Mayer* <katja.mayer at univie.ac.at 
<mailto:katja.mayer at univie.ac.at>>, Department of Science and Technology 
Studies, University of Vienna, and ZSI Centre for Social Innovation Vienna
*Hemank Lamba* <hlamba at cs.cmu.edu <mailto:hlamba at cs.cmu.edu>>, School of 
Computer Science, Carnegie Mellon University
*Claudia Müller-Birn* <clmb at inf.fu-berlin.de 
<mailto:clmb at inf.fu-berlin.de>>, Institute of Computer Science, Freie 
Universität Berlin
Am 22.01.19 um 14:00 schrieb Katja Mayer:
> Dear Colleagues,
>
> We would like to point your attention to a current call for papers for 
> a special issue to be published with Frontiers in Big Data entitled 
> "Critical Data and Algorithm Studies".
>
> Deadlines: 26 March 2019, abstract; 16 September 2019, manuscript.
>
> This special issue is dedicated to bringing together critical 
> expertise of scientists in data-driven research areas, who reflect 
> their daily routines, their methods, data sources and the social 
> impact of their research. We would also like to give space to those 
> experiences coming from newly established collaborations of computer 
> scientists with social scientists and humanities’ scholars, moreover 
> with policy makers, activists, or in citizen science projects. The 
> focus is on the critical reflection of scientific methods, data 
> sources, modeling, validation, replication, and review procedures 
> including questions of their impact regarding social behaviour, power 
> relations, ethics, and accountability, thus the performative and 
> normative aspects of data science practices.
>
> We welcome your papers to our peer-reviewed Article Collection. Papers 
> can be original research, reviews, or perspectives, among other 
> article types. More information:
>
> https://www.frontiersin.org/research-topics/9570/ 
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.frontiersin.org_research-2Dtopics_9570_&d=DwMFAg&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=6mIaugT4iJb5AOM8KpTYlasiiZKd58KfHip3ebCz6yw&s=d94EkKJF2ufL7aGfanUY5ePTcQnPPGLtjrqdrrm_90o&e=>
>
>  Katja Mayer and Jürgen Pfeffer
>
> ***
>
> Frontiers Gold Open Access: If you decide to publish with Frontiers, 
> your paper will be free to read for everyone. As an Open Access 
> publisher, Frontiers charges Article Processing Charge for accepted 
> papers (USD 950 for long articles; USD 450 for shorter ones). If your 
> institution or grant does not cover Open Access fees, simply apply for 
> a waiver. There are no financial barriers to publishing with 
> Frontiers. Frontiers also has 100+ institutional agreements with 
> universities and research organizations as well as 2 national deals. 
> Submissions will be judged on originality, interest, clarity, 
> relevance, correctness, language, and presentation (inter alia) by our 
> editorial board members 
> (https://www.frontiersin.org/journals/big-data/sections/data-mining-and-management#editorial-board). 
> If you have any questions related to charges or processes, please do 
> not hesitate to write to the editorial office at bigdata at frontiersin.org.
> _______________________________________________
> The Air-L at listserv.aoir.org mailing list
> is provided by the Association of Internet Researchers http://aoir.org
> Subscribe, change options or unsubscribe at: 
> http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
>
> Join the Association of Internet Researchers:
> http://www.aoir.org/

-- 
Dr. Katja Mayer
https://rri.univie.ac.at
https://www.katjamayer.net

Old address: School of Governance, Technical University Munich
Computational Social Science and Big Data
katja.mayer at hfp.tum.de