Theory of Knowledge - Course Companion for Students Marija Uzunova Dang Arvin Singh Uzunov Dang
spread of knowledge. The losses from suchthecan be incalculable and irrecoverable.incidentsthis reason, the Internet Archive has partialForof itself in several physical locations acrosscopiesgeopolitical contexts. What can we learndifferentthis project both for preserving humanfromas well as decentralizing the controlknowledgeknowledge? Is decentralizing the control ofofethos of the Internet Archive is that in mattersTheknowledge, access drives preservation. In otherofthe best way to preserve knowledge is towords,that it is accessible and in use. Inversely,ensuresays, “If you take things away from aKahleit’s as if it doesn’t exist”. His work isgeneration,not on a secure vault for safekeeping,modelledon a public library that is designed for use.butare potential barriers to use, and thereforeWhatto access, in a repository of humanbarriersproject’s goal is bold: universal access toTheknowledge. The Internet Archive is not justallthe internet, but digitizing books,backing-upand movies as well as recording over 100musicchannels 24 hours a day. The technologytelevisiondigitize recorded material already exists, but istoon the internet worth preserving, justeverythingwe are able to? How can we anticipate,becausewhat knowledge will be useful in thecorrectly,And can technologies to search andfuture?digital culture is an intensely visualToday'sPeople appear in photos produced onculture.own devices, tagged by friends, capturedtheirsecurity cameras, in the backgroundbyor otherwise) in the photos of(intentionallyin public spaces—all of which getstrangersinto streams of data. When these getsiphonedwith powerful face-detection technology,coupleddigital technologies see muchHowever,than physical appearance. They includemoreinsights into our likes, uses ofbehaviouraland reactions, interactions andlanguagealgorithms in criminal justice that calculateoflikelihood an accused will reoffend (calledtherisk): this risk score feeds into otherrecidivismthat suggest the length of the prisonalgorithmsThe tragic irony is that longer prisonsentence.have been shown to increase the ratessentencesrecidivism. And so, technology in the USofsystem has been shown to discriminateprisonethnic and economic class lines. People ofalongtend to receive a higher recidivism riskcolourwhich means they tend to be given longerscore,sentences, which, due to the internalizedprisonof prison, means they have fewerexperienceonce released and contribute toopportunitiesrecidivism risk scores for others in theirhigherThis vicious feedback loop isneighbourhood.of many that has resulted in the grosslyoneincarceration of black men in thedisproportionateStates. To dive deeper into this example,Unitedthe link to Cathy O’Neil's talk at Googlefollow“Weapons of Math Destruction”. Sheaboutrecidivism risk specifically from 28:40.discussesterms: Cathy O’NeilSearchof Math Destruction”“Weaponsterms: ProPublicaSearchbias riskMachinethe link to the article Machine Bias:FollowAssessments in Criminal Sentencing”RiskPredictive knowledge produced by1.models has limitations andcomputeras does predictive knowledgecaveats,by humans. How can we knowproducedmachine predictions are more orwhetherreliable than human ones?lessWhat kind of knowledge is necessary2.be able to evaluate the validity andtoWho should decide what assumptions a3.like this should be based on?modelIII. Methods and toolsIII. Methods and toolsknowledge a desirable thing?knowledge such as the Internet Archive?YouTubeFor discussionMachine biascatalogue such a vast collection keepup?assessmentsIII.3 Using data to know humansand consider the following questions.individuals have good reason forconcern.neutrality of risk assessment algorithms?relationships. Consider, for instance, the use71
2013 a team of researchers published resultsInthey could predict personality traitssuggestingFacebook Likes. Their method—calledusingmodelling—laid the foundationpsychographicwhat has been called microtargetingforwith in depth in Chapter 2, IV.2). The(dealtKosinski, Stillwell, Graepel andresearchers,summarized their findings as follows.Youyoureligious and political views, personalityethnicity,intelligence, happiness, use of addictivetraits,parental separation, age, and gender.substances,model correctly discriminates betweenTheand heterosexual men in 88% ofhomosexualAfrican Americans and Caucasian Americanscases,95% of cases, and between Democrat andinin 85% of cases ...RepublicanPercentiles3.7 shows that the personality traitsFigureage of Facebook users is associated withandthey Like on Facebook. For example, thewhatof users who Liked “The Colbertextraversionbasic premise was that individualsTheirbe profiled from their FacebookcouldFigure3.8 shows how accurately theLikes.claim to be able to predict commonresearcherscharacteristics. They tested thepersonalof their predictions by comparing itaccuracyusers’ self-reported personality assessments,tofound that their model predicted genderandrace with over 90% accuracy. It predictedanddetails such as sexual orientation,otheraffiliation and religion with overpolitical80%accuracy.the lengths to which this thesisGivenbeen implicated in alleged politicalhasit is worth considering themanipulation,and methodology behind it.assumptionsscience was fairly straightforward; whatTherevolutionary were the massive amountswas3III. Methods and toolsCase studyDigital technology, social networksand psychometricsaccessible digital records of behavior,…EasilyLikes, can be used to automaticallyFacebookaccurately predict a range of highly sensitiveandattributes including: sexual orientation,personalReport” was relatively low.(Kosinski et al 2013)of data the researchers could use to discernOpenness Conscientiousness Extraversion Agreeableness Stability Age100100100100100100Mitt RomneyMitt RomneyThe ColbertReportNicki Minaj909090909090Mitt RomneyCampingCamping808080808080The ColbertBarackReportBarack ObamaReportObama7070Barack Obama70Nicki Minaj70Cuddling7070CuddlingThe Colbert6050Hello KittyCamping6050CuddlingReport60506050Barack ObamaReport6050Cuddling6050CuddlingNicki MinajHello KittyHello Kitty404040404040Nicki Minaj303030303030Hello KittyHello Kitty202020202020Mitt RomneyReport101010101010000000Figure 3.7 Personality traits, age and Likes on Facebook72
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2013 a team of researchers published results
In
they could predict personality traits
suggesting
Facebook Likes. Their method—called
using
modelling—laid the foundation
psychographic
what has been called microtargeting
for
with in depth in Chapter 2, IV.2). The
(dealt
Kosinski, Stillwell, Graepel and
researchers,
summarized their findings as follows.
Youyou
religious and political views, personality
ethnicity,
intelligence, happiness, use of addictive
traits,
parental separation, age, and gender.
substances,
model correctly discriminates between
The
and heterosexual men in 88% of
homosexual
African Americans and Caucasian Americans
cases,
95% of cases, and between Democrat and
in
in 85% of cases ...
Republican
Percentiles
3.7 shows that the personality traits
Figure
age of Facebook users is associated with
and
they Like on Facebook. For example, the
what
of users who Liked “The Colbert
extraversion
basic premise was that individuals
Their
be profiled from their Facebook
could
Figure3.8 shows how accurately the
Likes.
claim to be able to predict common
researchers
characteristics. They tested the
personal
of their predictions by comparing it
accuracy
users’ self-reported personality assessments,
to
found that their model predicted gender
and
race with over 90% accuracy. It predicted
and
details such as sexual orientation,
other
affiliation and religion with over
political
80%accuracy.
the lengths to which this thesis
Given
been implicated in alleged political
has
it is worth considering the
manipulation,
and methodology behind it.
assumptions
science was fairly straightforward; what
The
revolutionary were the massive amounts
was
3III. Methods and tools
Case study
Digital technology, social networks
and psychometrics
accessible digital records of behavior,
…Easily
Likes, can be used to automatically
accurately predict a range of highly sensitive
and
attributes including: sexual orientation,
personal
Report” was relatively low.
(Kosinski et al 2013)
of data the researchers could use to discern
Openness Conscientiousness Extraversion Agreeableness Stability Age
100
100
100
100
100
100
Mitt Romney
Mitt Romney
The Colbert
Report
Nicki Minaj
90
90
90
90
90
90
Mitt Romney
Camping
Camping
80
80
80
80
80
80
The Colbert
Barack
Report
Barack Obama
Report
Obama
70
70
Barack Obama
70
Nicki Minaj
70
Cuddling
70
70
Cuddling
The Colbert
60
50
Hello Kitty
Camping
60
50
Cuddling
Report
60
50
60
50
Barack Obama
Report
60
50
Cuddling
60
50
Cuddling
Nicki Minaj
Hello Kitty
Hello Kitty
40
40
40
40
40
40
Nicki Minaj
30
30
30
30
30
30
Hello Kitty
Hello Kitty
20
20
20
20
20
20
Mitt Romney
Report
10
10
10
10
10
10
0
0
0
0
0
0
Figure 3.7 Personality traits, age and Likes on Facebook
72