IEEE Technology and Society Magazine - September 2019 - 83

of these risks. In this article, we highlight the potential dangers associated with using Big Data, particularly for human development.

Data Deluge
Over the last decades, widespread adoption of digital
applications has moved all aspects of human lives
into the digital sphere. The commoditization of the
data collection process due to increased digitization
has resulted in a "data deluge" that continues to intensify with a number of Internet companies dealing with
petabytes of data on a daily basis. The term "Big
Data" has been coined to refer to our emerging ability
to collect, process, and analyze the massive amount
of data being generated from multiple sources in
order to obtain previously inaccessible insights. Big
Data can equip policy and decision makers with evidence-based actionable insights that can help in
enhancing social systems, tracking development progress, and in developing a nuanced understanding of
the effects of policies without being swayed by intuition, ideologies, or emotions.
In particular, the recent advances in Machine Learning (ML) and Artificial Intelligence (AI) techniques have
revolutionized intelligent data analytics resulting in
enhanced interest in using Big Data for (sustainable
human and social) development (BD4D). Many organizations and government institutions are exploring such
solutions in diverse fields such as healthcare, education, intelligence, fraud, and crime prevention [5].
Although Big Data technology offers great promise
[3], it is worth remembering that Big Data is not a silver
bullet and we may ignore the hard-earned statistical lessons on measurement bias, data quality, and inference
variation that have been earned through a hard toil and
sometimes, bitter experience only at our own peril.
While most writing on data is enthusiastic, new work
has started emerging that has begun to show how Big
Data can mislead and be used detrimentally [7], [20],
[36]. More than 50 years of research into artificial intelligence and statistical learning has shown that there is no
free lunch - i.e., there are no universally applicable
solutions [50] and that there are always trade-offs
involved [47].
The use of BD4D is transforming society in various
domains from education to prevention, diagnosis, and
treatment of illness [16]. It has improved the efficiency
and effectiveness of disaster management systems by
utilizing real-time community information [39]. In these
applications, data is emerging as a new economic
resource and used by companies, governments, and
even individuals to optimize everything [31]. The ultimate goal of the development sector seems to be what
is refer red to as forecast-based financing - a

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prediction-based and automatized funding and logistical
system based on big data analytics and smart contracts
that automatizes everything from funding to action in
the field [53].
Despite the great excitement around the Big Data
trend, Big Data is also pegged with criticism. The dark
side of Big Data is that data invariably contains some
biases and there is a fear that Big Data could erode privacy and threaten freedom when deployed for human
and social development. These issues become particularly serious, when Big Data is deployed for human subjects in the case of BD4D. For example, consider the
case where Big Data predictions about individuals are
used to punish people on their propensities rather than
on their actions. This denies human free will and their
capability to improve over time and effectively reinforces existing stereotypes [24].
Various studies have articulated common issues that
concern Big Data analytics interdisciplinary research
[13], [19], [33], [44] but a critical analysis of BD4D is
missing in the literature. Therefore, in this paper, we
discuss the caveats of Big Data analytics when it is
used for human development purposes. The purpose of
this article is to present a critical synthesis of the
diverse literature and put together various underlying
issues that affect BD4D significantly. In addition to critically evaluating BD4D risks, this paper also explores
potential remedies that can help mitigate the problems
of BD4D.

General Issues in Using Data for Decisions
Today, big corporations are investing their resources in
utilizing Big Data technology to uncover important correlations, customer preferences, market trends, and
other hidden patterns that can help boost their production efficiency by providing data-driven products and
services. Despite these great opportunities, Big Data
has come with a raft of potential challenges and pitfalls
that need to be considered. Some of them are discussed next.

Big Data is Reductionist
Big Data provides a granular view of extremely complex
and historically unprecedented problems by using particular vantage points from the available data. It only
focuses on specific types of questions, and seeks their
answers in a reductionist way by seemingly ignoring
concrete contextual realities about human societies and
places. It is also crude and reductionist in its techniques
and algorithms by sacrificing complexity, specificity,
and deep contextual knowledge [26]. In this way, analyses are reduced to simple outputs that critically ignore
the underlying complexity of the actual processes and
entities being modeled.

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IEEE Technology and Society Magazine - September 2019

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