Message posted on 05/03/2019
CfP Crititcal Data Science workshop ICWSM 2019 in Munich
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 <br>_______________________________________________ <br>EASST's Eurograd mailing list <br>Eurograd (at) lists.easst.net <br>Unsubscribe or edit subscription options: http://lists.easst.net/listinfo.cgi/eurograd-easst.net <br> <br>Meet us via https://twitter.com/STSeasst <br> <br>Report abuses of this list to Eurograd-owner@lists.easst.netview formatted text
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