[ecrea] precon Big Data: Critiques and Alternatives

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

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 corporate1 and state interests2 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

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 We are seeking original, unpublished contributions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 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)
<mailto:(gelmer /at/ ryerson.ca)> and (gana /at/ yorku.ca) <mailto:(gana /at/ yorku.ca)>

_______________________________________________________________________________________________

1 See, for instance, the infamous Facebook mood experiment.(Kramer et
al., 2014).

2 See, for instance:
http://www.theguardian.com/world/2014/jul/08/darpa-social-

networks-research-twitter-influence-studies

3 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/>

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-------------------------------------------------------
Greg Elmer, PhD
Professor of Media, Communication & Culture
Bell Media Research Chair & Director, Infoscape Research Lab
Ryerson University, Toronto
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