CfP Crititcal Data Science workshop ICWSM 2019 in Munich
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.
With best regards
CALL FOR PAPERS
Workshop on Critical Data Science
at the 13th International AAAI Conference on Web and Social Media
Munich, Germany, June 11, 2019
Submissions deadline: March 25, 2019
Acceptance notification: April 12, 2019
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
, 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.
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
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-sciencefor more
information and submission instructions.
Momin M. Malik, Berkman Klein Center for
Internet & Society at Harvard University
Katja Mayer, Department of Science and Technology
Studies, University of Vienna, and ZSI Centre for Social Innovation Vienna
Hemank Lamba, School of
Computer Science, Carnegie Mellon University
Claudia Mller-Birn*, Institute of Computer Science, Freie
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