Engineering the public: Big data, surveillance and computational politics
Digital technologies have given rise to a new combination of big data
and computational practices which allow for massive, latent data
collection and sophisticated computational modeling, increasing the
capacity of those with resources and access to use these tools to carry
out highly effective, opaque and unaccountable campaigns of persuasion
and social engineering in political, civic and commercial spheres. I
examine six intertwined dynamics that pertain to the rise of
computational politics: the rise of big data, the shift away from
demographics to individualized targeting, the opacity and power of
computational modeling, the use of persuasive behavioral science,
digital media enabling dynamic real-time experimentation, and the growth
of new power brokers who own the data or social media environments. I
then examine the consequences of these new mechanisms on the public
sphere and political campaigns.
On the surface, this century has ushered in new digital technologies
that brought about new opportunities for participation and collective
action by citizens. Social movements around the world, ranging from the
Arab uprisings to the Occupy movement in the United States (Gitlin,
2012), have made use of these new technologies to organize dissent
against existing local, national and global power
[
11].
Such effects are real and surely they are part of the story of the
rise of the Internet. However, history of most technologies shows that
those with power find ways to harness the power of new technologies and
turn it into a means to further their own power (Spar, 2001). From the
telegraph to the radio, the initial period of disruption was followed by
a period of consolidation in which challengers were incorporated into
transformed power structures, and disruption gave rise to entrenchment.
There are reasons to think that the Internet’s trajectory may have some
differences though there is little reason to think that it will escape
all historical norms.
The dynamics outlined in this paper for computational politics
require access to expensive proprietary databases, often controlled by
private platforms, and the equipment and expertise required to
effectively use this data. At a minimum, this environment favors
incumbents who already have troves of data, and favors entrenched and
moneyed candidates within parties, as well as the data–rich among
existing parties. The trends are clear. The selling of politicians — as
if they were “products” — will become more expansive and improved, if
more expensive. In this light, it is not a complete coincidence that the
“chief data scientist” for the Obama 2012 campaign was previously
employed by a supermarket to “maximize the efficiency of sales
promotions.” And while the data advantage is held, for the moment, by
the Democratic party in the United States, it will likely available to
the highest bidder in future campaigns.
A recent peek into public’s unease with algorithmic manipulation was
afforded by the massive negative reaction to a study conducted by
Facebook and Cornell which experimentally manipulated the emotional
tenor of the hundreds of thousands of people’s newsfeed in an effort to
see if emotional contagion could occur online (Kramer, et al.,
2014). The authors stated their results “indicate that emotions
expressed by others on Facebook influence our own emotions, constituting
experimental evidence for massive–scale contagion via social networks.”
While both the level of stimulus and the corresponding effect size were
on the small side, the broad and negative reaction suggests that
algorithmic manipulation generates discomfort exactly because it is
opaque, powerful and possibly non–consensual (study authors pointed to
the Facebook’s terms–of–service as indication of consent) in an
environment of information asymmetry.
The methods of computational politics will, and already are, also
used in other spheres such as marketing, corporate campaigns, lobbying
and more. The six dynamics outlined in this paper — availability of big
data, shift to individual targeting, the potential and opacity of
modeling, the rise of behavioral science in the service of persuasion,
dynamic experimentation, and the growth of new power brokers on the
Internet who control the data and algorithms — will affect many aspects
of life in this century. More direct research, as well as critical and
conceptual analysis, is crucial to increase both our understanding and
awareness of this information environment, as well as to consider policy
implications and responses. Similar to campaign finance laws, it may be
that data use in elections needs regulatory oversight thanks to its
effects on campaigning, governance and privacy. Starting an empirically
informed, critical discussion of data politics now may be the first
important step in asserting our agency with respect to big data that is
generated by us and about us, but is increasingly being used at us
