[Air-L] CFP - Big Data: Critiques and Alternatives

[ganaele]

*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We are seeking original, unpublished contributions that explore critical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 at ryerson.ca and gana at 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-twitter-influence-studies

[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-it-worse/
>

-- 
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