Theory of Knowledge - Course Companion for Students Marija Uzunova Dang Arvin Singh Uzunov Dang

spread of knowledge. The losses from such
the
can be incalculable and irrecoverable.
incidents
this reason, the Internet Archive has partial
For
of itself in several physical locations across
copies
geopolitical contexts. What can we learn
different
this project both for preserving human
from
as well as decentralizing the control
knowledge
knowledge? Is decentralizing the control of
of
ethos of the Internet Archive is that in matters
The
knowledge, access drives preservation. In other
of
the best way to preserve knowledge is to
words,
that it is accessible and in use. Inversely,
ensure
says, “If you take things away from a
Kahle
it’s as if it doesn’t exist”. His work is
generation,
not on a secure vault for safekeeping,
modelled
on a public library that is designed for use.
but
are potential barriers to use, and therefore
What
to access, in a repository of human
barriers
project’s goal is bold: universal access to
The
knowledge. The Internet Archive is not just
all
the internet, but digitizing books,
backing-up
and movies as well as recording over 100
music
channels 24 hours a day. The technology
television
digitize recorded material already exists, but is
to
on the internet worth preserving, just
everything
we are able to? How can we anticipate,
because
what knowledge will be useful in the
correctly,
And can technologies to search and
future?
digital culture is an intensely visual
Today's
People appear in photos produced on
culture.
own devices, tagged by friends, captured
their
security cameras, in the background
by
or otherwise) in the photos of
(intentionally
in public spaces—all of which get
strangers
into streams of data. When these get
siphoned
with powerful face-detection technology,
coupled
digital technologies see much
However,
than physical appearance. They include
more
insights into our likes, uses of
behavioural
and reactions, interactions and
language
algorithms in criminal justice that calculate
of
likelihood an accused will reoffend (called
the
risk): this risk score feeds into other
recidivism
that suggest the length of the prison
algorithms
The tragic irony is that longer prison
sentence.
have been shown to increase the rates
sentences
recidivism. And so, technology in the US
of
system has been shown to discriminate
prison
ethnic and economic class lines. People of
along
tend to receive a higher recidivism risk
colour
which means they tend to be given longer
score,
sentences, which, due to the internalized
prison
of prison, means they have fewer
experience
once released and contribute to
opportunities
recidivism risk scores for others in their
higher
This vicious feedback loop is
neighbourhood.
of many that has resulted in the grossly
one
incarceration of black men in the
disproportionate
States. To dive deeper into this example,
United
the link to Cathy O’Neil's talk at Google
follow
“Weapons of Math Destruction”. She
about
recidivism risk specifically from 28:40.
discusses
terms: Cathy O’Neil
Search
of Math Destruction”
“Weapons
terms: ProPublica
Search
bias risk
Machine
the link to the article Machine Bias:
Follow
Assessments in Criminal Sentencing”
Risk
Predictive knowledge produced by
1.
models has limitations and
computer
as does predictive knowledge
caveats,
by humans. How can we know
produced
machine predictions are more or
whether
reliable than human ones?
less
What kind of knowledge is necessary
2.
be able to evaluate the validity and
to
Who should decide what assumptions a
3.
like this should be based on?
model
III. Methods and tools
III. Methods and tools
knowledge a desirable thing?
knowledge such as the Internet Archive?
YouTube
For discussion
Machine bias
catalogue such a vast collection keepup?
assessments
III.3 Using data to know humans
and consider the following questions.
individuals have good reason forconcern.
neutrality of risk assessment algorithms?
relationships. Consider, for instance, the use
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