A review of the six societal issues related to big data and privacy, including: - Perception - The necessity of data sharing - Cost reduction - Public mistrust - Hubris & Hyperbole

1. Societal Issues & Big Data
Sylvia Ogweng
March 2015
Concerns Associated
With Big Data And Its Uses

2. Societal
Issues:
How is big data perceived?
The necessity of data sharing to
validate research results.
Reducing costs and increasing
productivity with big data;
sparking the next wave of
scientific progress
Public mistrust
Saving us from ourselves: when
computers help us avoid incorrect
decisions
Hubris & hyperbole in Big Data
Six
Important
Topics
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3. How is
Big Data
Perceived?
Gumshoe Hypothesis: Hoarding information about individuals
for investigative purposes.
Big Brother Hypothesis: Collecting information about a
population of individuals to control ever member of the
population.
Borg Hypothesis: Collecting information about a population of
individuals to learn everything about the population.
George Carlin Hypothesis: A place to put a lot of stuff.
Scavenger Hunt Hypothesis: Big Data is a collection of
everything, created for the purpose of searching for individual
items and facts.
Egghead Hypothesis: Collecting information to draw
generalized scientific conclusions.
Facebook Hypothesis: A social archive that generates money.
Nihilist Hypothesis: Big Data does not exist as a definable field
of endeavour; it represents what we have always done, but with
more data.
Eight
Hypotheses by
Jules Berman
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4. The
Necessity of
Data Sharing
Scientists prepare data to serve a particular
scientific purpose, and sometimes the data serves
unanticipated goals.
Two examples in history:
• Astronomer Tycho Brache (16th century) gave
his charts to Johannes Kepler —> Kepler used
the charts to develop three general laws
describing the movements of the planets —>
Newton’s Laws of Motion
• Climate data indicates that the average power
of tropical cyclones has increased in the past
few decades. Also, ocean temperatures have
also increased in the same time frame. —> New
method for correlating the intensity of tropical
storms and hurricanes with ocean
temperatures.
Historical
Examples
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5. • Past decade has witnessed a powerful movement
among scientists to share data.
• Without access to primary data, there is no way to
verify final conclusions of experiments, and
research cannot be extended beyond questions
contained in the original manuscript.
• U.S. National Academy of Sciences has called for
scientists to provide publishers with the primary
data that supports the conclusions contained in
their manuscripts.
• Scientific Mischief: Big Data provides a multitude
of opportunities for scientific mischief - poor data,
poor data annotation, poor analyses, distorted
visualization, skewed interpretations, etc.
• Scientific Credibility: In the realm of Big Data,
scientific credibility is based on public access to the
primary data and to all the protocols accounting
for the collection, annotation, extraction, and
analysis of the data.
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The
Necessity of
Data Sharing

6. The
Necessity of
Data Sharing
De-identification of data is difficult to do.
Common diversions from organizations
include:
• “We’re working on it”
• “Some of our data has been de-identified
and released to the public”: leaves users
with small data set. Stifles creativity.
• “We provide full access to our staff”: staff
restricted to analysis that benefits the
company
• “We access to our data to trusted
individuals outside our institution”
Conflicts that
Exists With
Data Sharing &
Privacy
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7. The
Necessity of
Data Sharing
Set of recommendation for de-
identification for the healthcare
industry:
• Collect a variety of public domain
algorithms and software
implementation that can be used for
the common tasks in data de-
identification.
• Assign unique code to identify each
patient within a healthcare
environment.
Conflicts that
Exists With
Data Sharing &
Privacy
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8. Reducing
Costs &
Increasing
Productivity
in Science
• For many fields of science, the
traditional approach to
experimentation has reached its
limits. Lack of money and time to do
the research the old-fashioned way.
• Growing work force; increased
funding. Modern science has reached
a state of diminishing returns on
investment.
• Funders of medical research are
learning that there is simply not
enough money or time to conduct all
the clinical trials needed to advance
medical science at a pace
comparable to the first half of 20th
century.
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9. How can Big Data address this problem?
• Accelerate scientific progress by giving
research data that can expand or
bypass the clinical trial process.
• Validation tool for small data
experiments. Using Big Data it may be
feasible to confirm that experimental
findings based on small, prospective
studies are consistent with observations
made on very large populations.
• Drug Trials: Big Data may uncover
potential side effects and non
responsive subpopulations.
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Reducing
Costs &
Increasing
Productivity
in Science

10. • Reluctance to share data is based on
mistrust
• Corporations, medical centres, and
other entities have a fiduciary
responsibility to individuals whose
data is held in their repositories.
Sharing would violate privacy.
• De-identification of data is key.
• Zero risk is not obtainable.
• Without data, society is less safe, less
healthy, less smart.
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Public
Mistrust

11. Computers can play a crucial role in
helping us avoid incorrect decisions.
Examples in health:
• Drug prescription errors such as
suspected abuse
• Blood transfusion errors
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Saving Us
From
Ourselves

12. • Assertion based on Big Data analyses
can sometimes be validated, but they
can never be proven true.
• To obtain proof, you must enlist the
help of a mathematician.
• Trends, clusters, and so on, may
appear to be valid over a limited
range of observations, but may fail
miserably in tests conducted over
time with a broader range of data.
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Hubris &
Hyperbole

13. The Future
of Big Data
• Big Data is a social endeavour
• The future directions of Big Data will be
strongly influenced by social, political
and economic forces
• Public archiving, adopting useful
standards…how scientists use data over
the next few years may provide the
strongest argument for or against the
proliferation of Big Data resources.
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14. Principles of Data
Jules J. Berman
References