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

always be used for good or bad”. Technology
can
the values, biases and ideas of its
mirrors
In this view, technology can be
developers.
sexist and otherwise biased towards any
racist,
the conceptual building block of
Consider
technologies: models. We consider
many
throughout this book. A mathematical
these
is as much a feat of technology as the
model
that forecast limits to growth or the
simulations
data analyses run by Cambridge Analytica.
big
has argued that such models “are
O’Neil
embedded in mathematics”, with all
opinions
subjectivity and fallibility of their human
the
but that they are often assumed or
creators,
to be “neutral”. To complicate matters
presented
the inner workings of these technologies
further,
not visible to the vast majority of people,
are
including regulators, either due to barriers
often
cites an example of a teacher performance
O’Neil
system that used algorithms to
measurement
teachers according to a comparison of their
rank
test scores with their predicted scores
students’
by, you guessed it, a separate model).
(predicted
at the bottom of the distribution were
Teachers
But O’Neil argues there was almost no
fired.
between a teacher’s scores over
correlation
years: that they were effectively
subsequent
fired at random, according to the random
being
of students to their classes. The class
allocation
were too small and the model’s predictive
sizes
unproven, but the technology inspired
power
confidence among administrators to fire
enough
teachers. O’Neil cites examples of money
their
and car insurers that use factors such
lenders
whether applications are spelled correctly to
as
the interest rate a customer must pay.
determine
result is that people from lower educational
The
can pay higher car insurance even
backgrounds
institutions are better at testing their
Some
because of their underlying incentives.
models
continuously tests and improves
incentive,
models that predict consumer purchase
its
In contrast, those responsible for
behaviour.
US prison system have been much less
the
in testing and improving their
proactive
stop recidivism, as we saw earlier
modelsto
models can also result in
Credit-scoring
feedback loops: a person with a low
unforeseen
score cannot borrow cheaply, so has to
credit
high-interest rate loans that are even harder
use
pay off. Credit scoring models then further
to
that person.
downgrade
of the questions at the beginning of this
One
asked you to consider the extent to
chapter
1. Has your answer changed? If so, how?
If this is the first time you are considering
2.
question, what would you say are the
this
that make some technological risks
factors
unknowable?
What are the implications of this for how
3.
produce and apply knowledge using
we
the people at the heart of developing new
Are
best placed to judge the ethical
technologies
blowing has been a controversial
Whistle
over the last decades, associated with
practice
Edward Snowden and Christopher
Wikileaks,
(who brought attention to the Cambridge
Wylie
story). What factors affect whether
Analytica
blowing in technology is a positive
whistle
IV. Ethics
IV. Ethics
givenpopulation.
inthis chapter.
For ref lection
Technological risks
of expertise or intellectualproperty.
which risks of technology can be known.
technology?
IV.3 Responsibilities of
technologists
implications of their work?
if they are better drivers.
ethical practice?
Amazon, the online retailer with a clear profit
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