*International Communication Association Preconference* *Big Data: Critiques and Alternatives* *Event date: Thursday, June 9, 2016, Hilton at Fukuoka, Japan* *Deadline for proposals (500 words): December 15, 2015* *Deadline for discussion papers (3500-5000 words): May 9, 2016* *Please upload proposals to: * *https://easychair.org/conferences/?conf=ica2016 <https://easychair.org/conferences/?conf=ica2016>* *Organizers: Greg Elmer (Ryerson University), Ganaele Langlois (York University), Alison Powell (London School of Economics), Alessandra Renzi (Northeastern University)* The relationship between big data and the social science and humanities is, to say the least, contested. Big data – the automated collection, bundling together and algorithmic processing of massive datasets– at first answers to the historical limits of the social scientific approach: it seemingly overcomes sampling biases and allows for transdisciplinary research into complex questions. For instance, big data helps understand the consequences of global warming and the outcomes of armed conflicts and economic crises. At the same time, the cooptation of big data by corporate[1] <#_ftn1> and state interests[2] <#_ftn2> for purposes of surveillance and manipulation highlights a crucial limitation: big data is being developed as a tool of predictability and therefore as a tool for social and economic control. It is envisioned mostly as a means of establishing certainty about the present and the future, and of punishing statistical outliers and so-called risky behaviours. [3] <#_ftn3> The goal of this preconference is to reflect on alternatives to big data as a predictive model for population control, management and manipulation. Can we envision a framework through which big data will cease to be necessarily surveillant or personally intrusive? What would constitute an ethics of big data use? Beyond control, what kinds of relations between humans, between humans and their environment, and between humans and non-humans could be built through big data? What might be the consequences of placing different actors – citizens, activists, or even animals and plants - at the centre of data collection paradigms? We are seeking original, unpublished contributions that explore critical and alternative paradigms, theories, methods (including arts-based methods) and case studies that work against the predictive, managerial uses of big data. We are particularly interested in contributions that not only critically examine the claims of predictability, but also engage with alternative concepts such as unknowability, uncertainty, serendipity and possibility. We are also seeking contributions that examine the relationships between researcher, data and the public, and that challenge the claim of neutral objectivity of big data to replace it with questions of care, involvement and engagement in many modes of communication and in relation to many forms of power. We envision that the pre-conference will cover the following themes: public accountability; big data commons; and big data activism. Examples follow below. *1. **Bringing public accountability to big data* - Uncovering the political economy of big data initiatives - Critiques of data extractivism - Mapping networks of influence and power in big data uses *2. **Big data commons* - ethics of big data - participatory big data projects - grassroots big data initiatives *3. **Big data and Activism* - Activist research methods in data-driven projects - Activism, art and big data - Alternative data visualization Please upload 500 words proposals to *https://easychair.org/conferences/?conf=ica2016 <https://easychair.org/conferences/?conf=ica2016>* by December 15, 2015. Selected participants will be asked to submit their discussion papers (3500-5000 words) on May 9, 2016 for circulation among participants. Organizers will invite selected presenters to contribute to a publication. Please direct any questions to: gelmer@ryerson.ca and gana@yorku.ca ------------------------------ [1] <#_ftnref1> See, for instance, the infamous Facebook mood experiment.(Kramer et al., 2014). [2] <#_ftnref2> See, for instance: http://www.theguardian.com/world/2014/jul/08/darpa-social-networks-research-... [3] <#_ftnref3> Noyes, Katherine. (2015). “Will big data help end discrimination – or make it worse?”, Fortune, January 15. < http://fortune.com/2015/01/15/will-big-data-help-end-discrimination-or-make-...
-- Ganaele Langlois Assistant Professor Department of Communication Studies York University M.A. Coordinator Joint Programme in Communication and Culture York/Ryerson Universities Associate Director Infoscape Centre for the Study of Social Media www.infoscapelab.ca