IEEE Technology and Society Magazine - September 2019 - 6

the question actually becomes, do
the design choices enabled by Big
Data and Machine Learning (BD/ML),
have the capacity to alter, diminish
and, in the limit perhaps, actually
"destroy" what it means to be fundamentally human.
To understand and recognize the
scale of action facilitated by BD/ML,
it can be observed that there have
been, in recent years, a number of
accelerating and converging developments in computer hardware,
software, and network connectivity, but also in social acceptability.
The net result has been firstly, that
if the body does it, then someone
almost certainly has developed a
device or a sensor to measure it;

Technological design choices can
affect societal relationships within
a community.
and secondly, if the device to measure it is manufacturable, someone
has surely minitiaturized that device
and shoehorned it into a wearable,
portable or implantable device [6].
Thus we can see daily activity and
experience trackers, biomarker and
bioactivity trackers, bio-enhancement devices, smart clothing, especially for runners, a whole host of
devices for sexual health, institutional devices like ankle monitors
and employee trackers, smart toilets, and more besides.
These devices generate vast
amounts of real-time data, which
in itself would not be so much of a
problem but for three factors. The
first is the willingness of people to
carry those devices voluntarily. Secondly, there is the corresponding

6

agency to bring about many states
of affairs.
So then, if we turn from the subject (Big Data, or rather, the design
choices facilitated by Big Data/
Machine Learning), to the object,
"what does it mean to be human" ...
well, what does it mean to human?
At which point, one asks a question that has, of course, occupied
philosophers, psychologists, and
other professions for some thousands of years. Clearly, this article
is not going to provide a definitive
answer, but instead draws attention
to two dimensions of humanity: firstly, the authority and opportunity of a
"demos" to make decisions and "get
things done," and secondly, the relationship between the "demos" and
an empowered elite.
Collectively, one of the great
opportunities for what is being
called the Digital Transformation is
for the creation of a new generation
of socio-technical systems. One can
see potential applications of such
systems everywhere: from the fair
distribution of electrical power in
decentralized community energy
systems based on demand-side
management; to systems for maintaining ambient social relations
in shared living arrangements or
workspaces; to peer production
systems with local pooling of tools,
machinery, and resources; and to
intelligent transportation, including (genuine) sha r ing economy
applications for ride-sharing and
journey-as-a-service (and goodness
knows we need to get away from the
archaic car-ownership model; however watching innovation disappear
behind a corporate firewall through
the "privatization of invention" is
hardly an improvement [7]).
But these systems face three critical problems:

change in people's habits, lifestyle,
and culture which ensures that they
are permanently "on grid" (i.e., and
with the social acceptance that  -
indeed, almost expectation that -
people will be continually consulting
these devices). And thirdly, there
is the opportunity for personalization and identification from that
data: given "enough" data from a
large group, and a few data points
of an individual, some relatively
simple mathematics (such as logistic regression) can be used to make
reliable predictions about behavior,
preferences, political affiliation, and
so forth.
Moreover, these devices are permanently connected to a global
communication network to
transport that personalized
data. They are linked to a
massive expansion of computing power and system
architectures (from centralized warehouse-sized
server farms to distributed
peer-to-peer) to process
that data on unprecedented
scales. This has effectively
made some relatively old
algorithms and data structures designed for particular tasks computationally feasible: for example,
maintaining consistency in a distributed database (e.g., distributed
consensus technology like blockchain), or binar y classification
(e.g., neural networks, repackaged
as "deep learning"). It has also
made it possible for some old business models to masquerade as
new ones: e.g., brokerage or middleman relabeled as the "platform
revolution," or lotteries: taking a
small amount of money from a lot
of people equals a lot of money (i.e.,
how some cloud computing services operate).
The consequence is that BD/
ML facilitates design choices that
have the scope, the scale and the
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firstly, managing sustainability
of common-pool resources and

SEPTEMBER 2019
IEEE Technology and Society Magazine - September 2019

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