Table Of ContentData Democracy
fi
At the Nexus of Arti cial Intelligence,
Software Development, and
Knowledge Engineering
Edited by
Feras A. Batarseh
Ruixin Yang
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To the sons and daughters of the digital prison; as we may
give you freedom through a data democracy, you must not
inherit our thoughts or our ways.
To: Aaron Swartzdthe creator of the Open Access Manifesto.
I remember one day visiting the John Crerar Library in Chicago. My father (Aaron’s
grandfather) had spoken to me about it manytimes, about how he had done research there
whenhewasayoungman.ThelibraryisnowonthecampusoftheUniversityofChicago.Itis
an interesting place, as it is a science library whose mission is that it be open to the public.
Although the University of Chicago does not encourage public use any more, if you are
assertive, they will let you in.
Aaron’s grandfather taught me how to do library research when I was very young. We
hadmanyreferencebooksathomeandusedourlocalpubliclibraryoften.Tohim,theability
to do research was a fundamental skill to be passed on from father to son.
So I took Aaron to the Crerar Library and showed him around and showed him the
stacks and all the books that were there. I remember clearly taking a random book off the
shelfanddiscoveringthatitwasfromthe19thcenturyandexplainingtohimhowimportant
it was having access to the world’s knowledge. Aaron understood the importance of written
knowledge and, just as Crerar wanted his library open to the public, how it was vital that
everyone should be able to easily access the world’s research and knowledge. As Wikipedia
pointsout:“Becausethelibrarywasincorporatedunderthe1891speciallaw,courtapproval
wasrequiredforthemerger,aconditionofthemergerwasthatthecombinedlibrarywould
also remain free to the public.” We forget that in the last century, all the world’s knowledge
was available in the librariesdthere books and journals were accessible and open to
everyone.Aaronfoughtsothatinthisworldofbitsandbyteswecouldonceagainreturntoa
place where everyone could have access to the world’s knowledge and research.
Robert Swartz (Aaron’s father)
2019
Contributors
Feras A. Batarseh Graduate School of Arts & Sciences, Data Analytics Program,
Georgetown University, Washington, D.C., United States
Justin Bui Department of Electrical Engineering and Computer Sciences, University
of California, Berkeley, CA, United States
Deri Chong VolgenauSchoolofEngineering,GeorgeMaonUniversity,Fairfax,VA,
United States
Sam Eisenberg DepartmentofMathematics,UniversityofVirginia,Charlottesville,
VA, United States
Jay Gendron United Services Automobile Association (USAA), Chesapeake, VA,
United States
José M. Guerrero Infoseg, Barcelona, Spain
Debra Hollister Valencia College eLake Nona Campus, Orlando, FL,UnitedStates
Dan Killian Massachusetts Institute of Technology Operations Research Center,
Cambridge, MA, United States
Erik W. Kuiler George Mason University, Arlington, VA, United States
Ajay Kulkarni The Department of Computational and Data Sciences, College of
Science, George Mason University, Fairfax, VA, United States
Abhinav Kumar Volgenau School of Engineering, George Mason University,
Fairfax, VA, United States
Kelly Lewis CollegeofScience,GeorgeMasonUniversity,Fairfax,VA,UnitedStates
Connie L. McNeely George Mason University, Arlington, VA, United States
Rasika Mohod VolgenauSchoolofEngineering,GeorgeMasonUniversity,Fairfax,
VA, United States
xiv Contributors
Patrick O’Neil College of Science, George Mason University, Fairfax, VA, United
States; BlackSky Inc., Herndon, VA, United States
Chau Pham CollegeofScience,GeorgeMasonUniversity,Fairfax,VA,UnitedStates
Diego Torrejon College of Science, George Mason University, Fairfax, VA, United
States; BlackSky Inc., Herndon, VA, United States
Ruixin Yang Geography and GeoInformation Science, College of Science, George
Mason University, Fairfax, VA, United States
Karen Yuan College of Science, George Mason University, Fairfax, VA, United
States
A note from the editors
If you consume or create data, if you are a citizen of the data republic (willingly or grudg-
ingly),andifyouareinterestedinmakingadecisionorfindingthetruththroughdata-driven
analysis, this book is for you. A group of experts, academics, data science researchers, and
industry practitioners gathered to write this book about data democracy.
Multiple books have been published in the areas of data science, open data, artificial
intelligence, machine learning, and knowledge engineering. This book, however, is at the
nexusofthesetopics.Weinviteyoutoexploreitandjoinusinoureffortstoadvanceamajor
cause that we ought to debate.
Thechaptersofthisbookprovideamanifestotodatademocracy.Afterreadingthisbook,
youareinformedandsuitablywarned!Youarealreadypartofthedatarepublic,andyou(and
allofus)needtoensurethatourdatafallintherighthands.Everythingyouclick,buy,swipe,
try, sell, drive, or fly is a data point. But who owns/should own that data? At this point, not
you!Youdonotevenhaveaccesstomostofit.Thenextbestempireofourplanetisonethat
owns and controls the world’s best dataset.
Thisbookpresentsthedatarepublic(inSection1),introducesmethodstodemocratizing
data (in Section 2), provides examples on the benefits of open data (for healthcare, earth
science, and psychology), and describes the path forward. Data democracy is an inevitable
pursuit, let us begin now.
Feras A. Batarseh
Assistant Teaching Professor, Graduate School of Arts & Sciences,
Data Analytics Program, Georgetown University,
Washington, D.C., United States
&
Research Assistant Professor,
College of Science George Mason University,
Fairfax, VA, United States
Ruixin Yang
Geography and GeoInformation Science, College of Science, George Mason University,
Fairfax, VA, United States
2019
Foreword
The data crisis is an operational and ethical litmus test which the monolithic technology
giants have badly failed. The corporations whose profit models depend on datadFacebook,
Google, Amazon, and othersdhave proven inept at safeguarding consumers’ personal data
andhavesooutragedthepublicbysharingandsellingpersonalinformationthatpoliticians
can credibly advocate that they should be torn apart like Ma Bell, a tech monopoly from an
earlier era. At the same time, most of the artificial intelligence (AI)ecentered digital corpo-
rationsthatdominatethetechindustryregardthedata theycollectasprotected intellectual
property.Theygotogreatpainstocollectthedataandconsidertheiraggregationamorethan
fair trade for “free” services such as Internet search, social networking, and shopping. They
will neither acknowledge that consumers have a legitimate claim to their own data nor give
uptheirproprietarystakeandmakethedata“open”andavailabletoeveryone;buttheyhave
proven again and again that they are not able, or fit, to manage it.
The short-term penalty for their data mishandling is lost customers. Generation Z-ers,
the first truly native digital citizens, are abandoning social media; 34% say they will leave it
entirely, while 64% say they are taking a break. Privacy concerns are high on their list of
reasons why [1]. The longer-term penalties are much worse and threaten us all; if the tech
giants cannot ethically manage or control services using machine learning, how will they
safeguard the world’s most sensitive technology as machine intelligence ineluctably grows?
Andhowwilltheydosowhenwesharetheplanetwithcomputersthatcanoutsmartusall?
Howdidwegettothisdangerousstateofaffairs?Tounderstand,wehavetogobacka
decade,whenbigdatawasalltherage.Then,companieswithalotoftransactionaldatacould
analyzeitusingdata-miningtoolsandextractusefulinformationlikeinefficienciesandfraud.
WallStreet usedbig data algorithmsto seek outinvestment opportunities andmaketrading
decisions. A big data technique called affinity analysis let companies discover relationships
amongconsumersandproductsandoffersuggestionsformovies,shoes,andothergoods.Big
data still serves up these enterprise-friendly insights. But around 2009, something really big
happened to big data.
Three scientistsdHinton, LeCun, and Bengio, all of whom would later win the presti-
gious A. M. Turing Prizedrevealed that training learning algorithms on big data yields pre-
dictive abilities that exceed hand-coded programs [2]. Soon this technique, called deep
learning, fueled amazing breakthroughs in speech recognition, computer vision, and self-
driving cars. Corporations everywhere caught on. Since 2009, thanks in large part to deep
learning, investment in AI has doubled each year, and now stands at about $30 billion. AI
implementation in enterprise grew 270% over the last four years, mostly, again, thanks to
deeplearningapplications.By2030,AIwilladdanestimated$15trilliontotheGlobalGDP[2].
Just the way that electricity powered the 20th century, this century’s economic
opportunities are driven by AI.
To get the latest AI applications to work, high quality datasets are mandatory. While
hackneyed,theaphorism“Dataisthenewoil”getstruerallthetime.Asdatagainvalue,their
acquisition, ownership, and use grow more controversial. To understand why, we must
consider what data are, and where data come from.
xviii Foreword
Data are discrete pieces of information, such as numbers, words, photographs, mea-
surements,anddescriptions. Bigdatarefers toacollectionofdata solarge thatit cannot be
storedorprocessedwithtraditionaldatabaseorsoftwaretechniques.Forexample,Snapchat
users share 527,760 photos every minute. Also every minute, 456,000 tweets are sent on
Twitter. All these data require hundreds of thousands of terabytes of storage (1 terabyte
equals1024gigabytes;1gigabyteequals1024megabytes;1megabyteequals1024bytes).It’s
estimatedthatGoogle,Amazon,Microsoft,andFacebooktogetherstore1.2millionterabytes
among them [3].
Who produces all these data? You! Or rather, your use of the Internet, social media,
digitalphotos,communicationslikephonecallsandtexts,andtheIoT,orInternetofThings.
Your digital activity generates mountains of data, more than half a gigabyte per day for an
average user [4]. AI’s recent boom can be partly explained by the fact that for the first time
enough large-scale data are available for high functioning machine learning systems (the
other two drivers of the AI revolution are GPU and AI-specific processor chips, and key
insights, i.e., deep learning).
How do the tech giants profit from data? In two ways. First, companies including
Facebook,Amazon,andGooglemakemoneybyofferingtheirclientscuratedadpositioning.
Basedonyourdigitalprofile,theytargetyoufortheirclients’ads.Second,wheneveryoubuy
theirproductorusetheirservice,thetechgiantsgatherdataaboutyouandyourwebactivity.
These data feed the development of profitable, data-hungry applications and products.
WheneveryoucommentonanAmazonproduct,tweetonTwitter,or“like”anoticeonyour
tennisleague’sFacebookpage,youarehelpingmintcashfortheworld’srichestcorporations.
Because we, the users, generate the data that are the lifeblood of these companies, we
should be paid for its use, right? Don’t you own your data?
On the Internet, you do not own your data in a traditional sense, the way, say, a
photographerownsherphotograph.IfyoupublishaphotographonFacebook,it’sstillyours
forpersonaluse,ofcourse.ButbyelectricallysigningFacebook’sTermsofUse,yougiveFB
permission to use your photograph as they see fit and to share your photograph with their
business partners and other entities. And there is a lot to share. For each user, on average,
Facebookhasasmuchas400,000MSWorddocumentsworthofdata.Googlehasmuchmore,
about 3 million MS Word docs per user [5].
Google makes the case that they divorce your identity from your data, and so your
privacy is safe with them. Their advertising clients target your “digital identity” with ads,
withouteverknowingyournameorotherpersonalinformation.Inessence,whoyouaredoes
not matter to Google. What matters are your photos, texts, and browsing history, and they
highlyprizetheiraccesstoit.Theydonotwantyoutohavedatarightsthatwillrestricttheir
unimpededuse,andtheycertainlydonotwanttomakeyourdataopenandfreetoanyone.
And despite the tech corporations’ promises, they do shamefully little to secure your
dataortohonortheirownTermsofUse.Thegreatestexamplesofarofhowlowcompanies
canstoopisataleofbigdata,foreignintrigue,andthemostimportantelectioninadecade:
Facebook’s Cambridge Analytica scandal.
Briefly, in 2011, due to past failures in keeping user data private, Facebook made an
agreement with the Federal Trade Commission (FTC). It required that, among other things,
Facebookreceive“prioraffirmativeconsent”fromusersbeforeitsharedtheirdatawiththird
parties.This“consentdecree”wastolast20years.Threeyearslater,in2014,Facebookallowed
an app developer to access the personal data of some 87 million users and their Facebook
“friends.”ThedeveloperworkedcloselywiththeelectionconsultingfirmCambridgeAnalytica.
It acquired these data and then created an algorithm that could determine personality traits
connected to voting behavior for the affected users. In conjunction with a Russian firm,
