Introduction to Data Science
Section 6
Data Matters 2015
Sponsored by the Odum Institute, RENCI, and
NCDS
Thomas M. Carsey
carsey@unc.edu
Data Science Examples in Action
Google Flu
• Link between search terms used in Google and
actual flu outbreaks.
• The idea is that people with the flu or who are
in areas where more folks have the flu will be
searching more about it.
• Baseline model used searches on 45 terms
related to flu and flu symptoms
– http://www.google.org/flutrends/about/how.html
Billion Prices Project @ MIT
• Collects prices from hundreds of online
retailers every day.
– http://bpp.mit.edu/
• Measures health of the economy in near real
time.
• Two days after Lehman Brothers failed in Sept.
2008, their index began to fall.
– Govt. released data in mid-November on October
figures before it could show similar evidence.
Medicare Fraud Detection
• 90 people charged with $260 million in false
billing
– https://www.fbi.gov/newyork/pressreleases/2014/medicare-fraud-strike-forcecharges-90-individuals-for-approximately-260million-in-false-billing
Twitter Trending
• Twitter trending
– https://twitter.com/WhatsTrending
Nate Silver
• FiveThirtyEight – http://fivethirtyeight.com/
Computational Event Data System
• Automated coding of English-language news
reports to generate political event data in the
Middle East, Balkans, and West Africa.
• The focus is on contentious political events
(bombings, shootings, protests, riots, etc.)
• Records initiator, target, location, “size”
– http://eventdata.parusanalytics.com/
Satellite Data and Crop Insurance
Fraud
• Mapping satellite images of fields (of 100 acres or
larger in Nebraska)
• Map “greenness” of the field against crop
insurance claims, then investigate claims on the
ground.
• Good predictor of fraud
– http://data2discovery.org/dev/wpcontent/uploads/2014/05/DMysnyk_Unsupervised_Le
arning_Classification.jpeg
Interest Groups and Policy Diffusion
Garrett and Jansa, forthcoming SPPQ
Plagiarizing Policy
Kroeger, State Politics Conference 2015
• Compares model bills with actual legislation
– 64 groups; 1434 model bills
– 7,542,937 versions of bills
– Results in 10,816,571,658 pairwise comparisons
• Measured similarity based on percentage of 5word strings that appear in both model and
actual bills.
– https://www.dropbox.com/sh/i4ud5e6tyap1qp2/AAC
RSIANXFLC8U5Ln9Bn6usca/Kroeger_SPPC.pdf?dl=0
Communication Networks
Desmarais et al.
• Email from New Hanover County, North
Carolina
– 30 Managers of departments of county govt.
– Feb., 2011
• 30,909 emails
• 8,097 authored by managers
• 1,739 sent to other managers
• Study within vs. between group
communication, broken down by topics
– http://people.umass.edu/bruced/pubs/Krafft_Mo
ore_Desmarais_Wallach_NIPS2012.pdf
Privacy, Ethics, Transparency
“Could” versus “Should”
• Advances in Data Science focus more on what
could be done than what should be done.
• Current law has not caught up with current
technology.
• Do people understand what they are giving away
about themselves?
• Are researchers being open about what they are
doing with data?
• Are companies?
• Are governments?
Data Access and Research
Transparency (DA-RT)
• Increased regulations from government to
provide access to data funded by government
dollars.
• Increased professional publication standards for
sharing research data
• Increased pressure for transparency in research
methods more generally, including data access
• Symposium in Political Science (PS: Political
Science and Politics, Vol. 47, Issue 01.)
– http://journals.cambridge.org/action/displayIssue?jid
=PSC&volumeId=47&seriesId=0&issueId=01
DA-RT Statement
• Commitment to DA-RT principles by academic
journals.
– Require authors to provide research data via a
trusted repository
– Require authors to provide clear instructions/code
used for analysis
– Maintain a consistent data citation policy
– Update all codes, guidelines, etc. as needed.
– http://media.wix.com/ugd/fa8393_da017d3fed82
4cf587932534c860ea25.pdf
Retracted Research
• Science article on changing minds on same-sex
marriage
• Donald Green and Michael LaCour
• http://retractionwatch.com/2015/05/20/authorretracts-study-of-changing-minds-on-same-sexmarriage-after-colleague-admits-data-were-faked/
• Who is responsible?
– Authors, publishers, advisors, IRBs, professional
societies, others?
Grown in Retractions
• NY Times: Nature reports a 10 fold increase in
retracted scientific research in 2000s compared
to 1990s.
• http://www.nytimes.com/interactive/2015/05/28/science/r
etractions-scientific-studies.html?_r=0
• http://www.nature.com/news/2011/111005/pdf/478026a.p
df
• Best guess is improved standards and technology
is finding more problems, not that bad behavior
itself is on the rise, but we don’t know.
Retracted Studies Live On
• Studies by John Budd at U. Missouri (School of
Ed.) finds that
– Retracted articles continue to be cited just as often as
non-retracted articles.
– Only 4-8% of citations mention the retraction
• There is an effort to deal with this called
CrossMark
– http://www.crossref.org/crossmark/
• More attention to retracted work at Retraction
Watch
– http://retractionwatch.com/
Other Retractions
• 1998 Study linking vaccines to autism retracted in 2010
• http://www.nytimes.com/2010/02/03/health/research/03lancet.h
tml
• 2014 article in Nature on Stem Cell production method
• http://www.nytimes.com/2014/07/03/business/stem-cellresearch-papers-are-retracted.html
• 2004 and 2005 papers in Science on human cloning
• http://www.nytimes.com/2005/12/31/science/amid-confusionjournal-retracts-koreans-stem-cell-paper.html
There is More
• John Darsee faked data in about 100 published
papers on heart diseas
• Study linking vaccines to autism
• Study touting a new method of producing stem
cells
• Two studies on human cloning
• Nearly 100 papers by John Darsee on heart
research
• 17 papers on Physics by J. Hendrik Schon
Privacy and Ethics
• Data, the elements of data science, and even
so-called “Big Data” are not new.
• One thing that is new is the greater variety of
data and, most importantly, the amount of
data available about humans.
• Discussion and good policy regarding privacy,
security, and the ethical use of data about
people lags behind the methods of collecting,
sharing, archiving, and analyzing data.
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The Free Market, Unfair Competition, Big Brother?
26
Privacy and Ethics
• Several questions emerge from the explosion of
digital data about us:
– Identity: What is the relationships between our
offline and online identities?
– Privacy: Who should control access to online data?
– Ownership: Who owns data? Can ownership rights be
transferred? What are the obligations of those who
generate and use data?
– Reputation: How can we determine if data is
trustworthy? How can we know if provider acquired
data legitimately?
• There are entire industries on reputation management.
Ethical Claims
• Davis claims “Big Data, like all technology, is
ethically neutral”
• However, business is a social/public enterprise, so
you will encounter the values others hold.
• Competing values means competing ethical
standards, and thus, ethical conflicts.
• Modern Big data magnifies this encounter
because it increases the points of connection.
• It also leaves a digital trail of those encounters.
Ethical Decision Points
• Inquiry: what are our values?
• Analysis: what are our current data handling
practices? Do they align with our values?
• Articulation: explicit written expression of
alignment and gaps between values and
practices
• Action: specific plans and activities used to
close those gaps.
Buying vs. Selling
• Company data policies:
– 34 of 50 Fortune-class companies said the would not
sell personal data.
– No company said explicitly that the would sell
personal data.
– No company made any explicit statement that they
would not buy personal data
– 11 companies said explicitly that buying personal data
was allowed.
• Why different standards for buying vs. selling
personal data?
Privacy
•
•
•
•
Physical Privacy
Informational Privacy
Organizational Privacy
Privacy of
– Our communications
– Our behavior
– Our person
• Is digital communal living different than physical
communal living?
• Who decides? Who regulates? Who Enforces?
Privacy as a Common Pool Resource?
• We all benefit from privacy norms, but we all may
realize personal gains by violating them.
• If everyone has that same incentive, the public
good gets destroyed.
• To govern our collective behavior:
– We need norms/rules that are agreed upon and
known
– We need effective monitoring
– We need a mechanism to sanction violations
• The question is, do we need an external force or
can we govern ourselves? (Elinor Ostrom’s work)
Closing Thoughts on Data
Science
Conclusions
• Data Science is an evolving field
– Exciting, confusing, immature
• Data science will be critical in an information
economy and to national security, but it is also
changing our social behavior, the arts, and everything
else.
• There are many claims made about data science and
“Big Data,” and some of them are probably true.
• Focused on applied interaction between computer
science, information science, and statistics.
– This is good, but . . .
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Conclusions (cont.)
• Data Science needs to figure out how to
include substantive expertise and theories.
– Finding patterns and gaining understanding are
two different tasks.
• Data Science needs to foster interdisciplinary
communication
– Data as the universal language
• Data Science needs greater attention to
privacy and ethics.