IEEE Technology and Society Magazine - September 2019 - 89

to collect the desired data to properly design an experiment rather than collecting all possible information.
Specifically, in the field of human development and
humanitarian action, there is no way around corroborating findings based on Big Data with intelligence gathered at the field level. This requires that international
organizations and development actors actively increase
their capacity for data collection and analysis also
referred to as "Humanitarian Intelligence" [52].

User-Friendly and Responsible Data Analytics
Big Data algorithms are not as trustworthy as typically
assumed since they draw upon data collected from a
prejudiced and biased world [4]. Cathy O'Neil [36]
describes how algorithms often perpetuate or worsen
inequality and injustice and suggests that there should
be laws and industry standards to ensure transparency
for Big Data gathering and utilization. In particular, false,
outdated, and taken out of context information may
cause harm to an individual's autonomy and the use of
such information should, therefore, be restricted. As a
remedy to this issue, "the right to be forgotten" enables
data subjects to reassert control over their personal
information. There might be fair audits of algorithms but
first, the awareness of this issue to programmers is
required as they share a disproportionate amount of
responsibility in the design of Big-Data-based algorithms.
Since AI and ML algorithms are embedded in many crucial social systems - ranging from crime fighting to job
portals to hospitals - it is recommended that social-systems analysis should also include the possible effects of
AI on their performance throughout the various stages
of the design cycle (i.e., conception, design, implementation, and deployment) in social institutions.
BD4D practitioners should follow the lead of the following five principles of data for humanity laid down in [51]:
1)
2)
3)
4)

Do no harm;
Use data to help create peaceful coexistence;
Use data to help vulnerable people and people in need;
Use data to preserve and improve the natural environment; and
5) Use data to help create a fair world without discrimination.
BD4D practitioners will also do well to adhere to the
following oaths, which were developed by Herman and
Wilmott as the "Modelers' Hippocratic Oath" [12] in the
light of the global financial crisis:
1) Though I will use models boldly to estimate value, I
will not be overly impressed by mathematics;
2) I will never sacrifice reality for elegance without
explaining why I have done so;

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3) Nor will I give the people who use my model false
comfort about its accuracy. Instead, I will make
explicit its assumptions and oversights; and
4) I understand that my work may have enormous
effects on society and the economy, many of them
beyond my comprehension.

Cautious Perspective on the Use of Big Data
In this paper, we have provided a cautious perspective on
the use of Big Data for human development. While we
believe that Big Data has great potential for facilitating
human development, our aim is to caution against an
uncritical acceptance and careless application of Big Data
methods in matters directly affecting human welfare and
development. We need to guard against a näıve overreliance on data to avoid the many pitfalls of data worship.
We argue that Big Data technology is a tool, and like all
tools it should be considered as a handmaiden rather
than as a headmaster. In particular, we argue that Big
Data analytics cannot substitute for good research design
and subject-matter knowledge. Various potential remedies to address the pitfalls of using Big Data for Human
and Social Development (BD4D) have also been highlighted. To conclude, we will like to emphasize that our paper
should certainly not be construed as a techno-phobic
manifesto. We believe strongly in the promise of BD4D
but when pursued with due attention to the mitigation of
the many associated pitfalls.

Author Information
Siddique Latif is with the University of Southern Queensland,
Australia, and with Information Technology University
(ITU)-Punjab, Pakistan.
Adnan Qayyum, Muhammad Usama, and Junaid
Qadir are with Information Technology University (ITU)Punjab, Pakistan.
Andrej Zwitter is with University of Groningen,
Netherlands.
Muhammad Shahzad is with National University of Sciences and Technology (NUST), Pakistan.

References

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[2] A. Acquisti, R. Gross, and F. Stutzman, "Privacy in the age of augmented reality," Proc. National Acad. Sciences, 2011.
[3] A. Ali, J. Qadir, R. Rasool, A. Sathiaseelan, A. Zwitter, and J. Crowcroft, "Big data for development: applications and techniques," Big
Data Analytics, voil. 1, no. 1, p. 2, 2016.
[4] P. Andras, L. Esterle, M. Guckert, T.A. Han, P.R Lewis, K. Milanovic, T. Payne, C. Perret, J. Pitt, S.T. Powers et al., "Trusting intelligent machines: Deepening trust within socio-technical systems,"
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