IEEE Technology and Society Magazine - September 2019 - 84

Big Data is not Neutral nor is it Objective
One of the compelling aspects of Big Data is the assumption that it is eliminating human subjectivity and bias.
Although the idea of "baggage-free" learning is seductive,
data alone is not enough since there is no learning without knowledge as a practical consequence of the "no free
lunch" theorem. Big Data analytics is what mathematicians call an ill-posed problem, thus no unique model
exists1; the only way to resolve some ill-posed problems
is to make additional assumptions. These assumptions
are subjective and can be thought of as being "opinions
embedded in Math" [36]. Therefore, while data is often
considered neutral, data collection techniques and analysis methods are designed by humans based on their
knowledge, experience, and beliefs.

An uncritical acceptance and
careless application of Big Data
methods can adversely affect human
welfare and development.
The German philosopher Heidegger wrote,
"Everywhere we remain unfree and chained to
technology, whether we passionately affirm or deny
it. But we are delivered over to it in the worst possible way when we regard it as something neutral."
Big Data scientists make computations using data
and present results about people in terms of numbers
or mathematical relations based on layers upon layers
of assumptions. Data scientists use this data and try to
organize it into objects, relations, and events, and in
doing so invariably analyze the data within the backdrop of their subjectivity and partiality. Moreover, it is
challenging to ensure fairness in ML algorithms due to
significant statistical limitations and it is possible that
enforcing fairness can harm the very groups that these
algorithms were supposed to protect [10].

Opaque Black-Box Models
Many of the modern mathematical models used for Big
Data analysis are opaque - and unregulated [36].
These models are often used inappropriately and can
scale up biases thereby producing misleading results.
1
A famous statistical quote by George Box is, "All models are wrong, some
are useful."

84

Such models have even been referred to as "weapons of
maths destruction" (WMD) due to their inscrutable black
box nature. These WMDs can cause havoc/harm and
tend to punish the poor due to vicious feedback loops
[36]. The reliance on opaque algorithmic black boxes for
decision making is even more problematic when we
consider how easy it is for adversaries to attack ML
models. New research attacks have emerged in which
adversaries can trick ML models to make decisions as
they desire - something that has huge consequences
for a society in which human decisions are automated
and made by machines.

Big Data's Big Bias Problem
Most Big Data datasets contain hidden biases both in
collection and analysis stage. These biases in data can
create an illusion of reality. These biases are hard to
undo and their elimination have unintended consequences on the results. Four major biases of Big Data
are described as follows.
Sampling Bias: When the samples are partial, selective, and not random, the patterns of omitted information may influence the structures discovered in the
data. Such samples will be unable to accurately predict
the outcomes and will have reduced validity [38]. Such
bias is often found when we focus on sentiment analysis
on social media, safety monitoring using social media,
or use population statistics and tourism statistics.
Activity Bias: This is another bias that is usually
encountered in web data [29]. This arises from the timebased correlation of users' activities across diverse websites, because most of the users visit a website for a
while and never return during the measurement period.
This can be explained using the example of the model
of Ginsberg et al. [18] for predicting flu cases. Using 50
million Google search terms, the model suffered from
activity bias where it started over-predicting the flu in
the U.S. [9], [28].
Information Bias: This refers to the delusion that
more information always results in better decisions.
Data scientists give too much significance on the volume of data and ignore other alternative opinions and
solutions. The irony of this belief is that fewer data can
give better decisions in some situations. In various
cases, a simple rule of thumb can work better than
complex analysis. There are situations where ignorance
from very large data and required calculation can provide more accurate and faster results because large
biased data just magnify the errors. Having more information is not always more desirable.
Inductive Bias (Assuming the Future Will be Like the
Past): In Big Data analysis, there is an implicit belief that
the future can always be extrapolated based on historical data. Apart from this approach being philosophically

IEEE TECHNOLOGY AND SOCIETY MAGAZINE

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SEPTEMBER 2019



IEEE Technology and Society Magazine - September 2019

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