Dear Colleagues,
<br>
<br>We invite submissions to the Workshop on Critical Data Science, taking
<br>place on June 11, 2019 at the 13th International AAAI Conference on Web
<br>and Social Media (ICWSM-2019) in Munich, Germany.
<br>
<br>With best regards
<br>
<br>Katja Mayer
<br>
<br>CALL FOR PAPERS
<br>*Workshop on Critical Data Science*
<br>at the 13th International AAAI Conference on Web and Social Media
<br>(ICWSM-2019),
<br>Munich, Germany, June 11, 2019
<br>
<br>https://projects.iq.harvard.edu/critical-data-science
<br>
<br>*Submissions deadline: March 25, 2019*
<br>*Acceptance notification: April 12, 2019*
<br>
<br>-------------
<br>The social world is far messier than technical training prepares one
<br>for. Among data scientists trained in fields like computer science and
<br>statistics are those experiencing a sense of vertigo: we start to
<br>realize both the ways in which modeling breaks down on human beings,
<br>requiring different notions of rigor, and the potentially negative
<br>social impacts of modeling, requiring responsible engagement and activity.
<br>
<br>We define critical data science as our vision of the practice of
<br>working with and modeling data (the data science), combined with
<br>identifying and questioning the core assumptions commonly underlying
<br>that practice (the critical). The workshop seeks to combine cultures
<br>of critique with those of practice, bringing together data scientists
<br>and scholars from computer science and the social sciences around
<br>responsibly carrying out data science on social phenomena, and creating
<br>sustainable frameworks for interdisciplinary collaboration.
<br>
<br>The workshop will involve short reflective presentations by
<br>participants, combined with a creative group-based activity to further
<br>support reflection of their own and neighboring scientific practices and
<br>to create opportunities for further cooperation. The workshop will
<br>conclude with a wrap-up for collecting resources and discussing future
<br>outcomes, and producing a draft compilation of best practices and a list
<br>of priorities for further engagement.
<br>
<br>Submissions may either be non-archival 2-page statements of interest or
<br>motivation, or archival papers up to 4,000 words. Accepted archival
<br>papers will be published in Workshop Proceedings of the 13th
<br>International AAAI Conference on Web and Social Media
<br>, a special issue of
<br>the journal Frontiers in Big Data. Open Access publishing costs will be
<br>waived for authors without institutional support for covering these fees.
<br>
<br>Topics include:
<br>
<br> * What should be standards and practices both of methodological rigor,
<br> and of respect for subjects, when carrying out computational
<br> research on social systems?
<br> * What role can discussions of methods and instruments play in larger
<br> critiques of the limitations of data science?
<br> * What are points of fundamental disagreement or diverging
<br> orientations/priorities between disciplines?
<br> * What can we learn from the long tradition of critical scrutiny in
<br> statistics?
<br> * What combinations of experiences and/or readings has led data
<br> scientists to recognize, and perhaps even adopt, non-technical
<br> ways of framing the world? How do and can these ways of knowing
<br> interact with a modeling approach?
<br> * What philosophical commitments or normative orientations, if adopted
<br> by data scientists, would produce a principled data science? How can
<br> those be realized in interdisciplinary teams?
<br> * What might it look like to use modeling critically and reflexively,
<br> or to contextualize what we can or cannot know from modeling from
<br> within the modeling process?
<br> * What can we learn from works looking at the social impact of
<br> implemented model-based systems?
<br> * What sorts of practices, coalitions, and collaborations can include
<br> marginalized voices into data science rather than exclude them?
<br> * Beyond a space for critical reflection, what can be the positive
<br> project of a critical data science?
<br> * How can we design collaborations in critical data science?
<br>
<br>See https://projects.iq.harvard.edu/critical-data-sciencefor more
<br>information and submission instructions.
<br>
<br>Contact: .
<br>
<br>ORGANIZERS
<br>*Momin M. Malik*, Berkman Klein Center for
<br>Internet & Society at Harvard University
<br>*Katja Mayer*, Department of Science and Technology
<br>Studies, University of Vienna, and ZSI Centre for Social Innovation Vienna
<br>*Hemank Lamba*, School of
<br>Computer Science, Carnegie Mellon University
<br>*Claudia Mller-Birn*, Institute of Computer Science, Freie
<br>Universitt Berlin
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