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

IV. Ethics
(using 840 billion words selected from
English
internet) become prejudiced through word
the
that mirror humans’ semantic
associations
such as flowers (nice) or insects (not
biases,
Additionally, the machines learned to
nice).
female names with family and male
associate
Figure 3.13 Example from Google Translate that went viral on Twitter in 2017
with career. According to researchers,
names
biases within the text of the internet
these
“recoverable and accurate imprints of our
are:
biases … whether morally neutral as
historic
insects or flowers [or] problematic as
toward
race or gender” (Caliskan et al 2017).
toward
Twitter chatbot called Tay, developed by Microsoft
A
released in March 2016, was shut down after
and
3
separate study published in 2017 in Science
A
that programs that teach themselves
reported
Turkish detected
English
o bir aşçi
she is a cook
he is an engineer
o bir mühendis
o bir doktor
he is a doctor
she is a nurse
o bit hemşire
o bir temizlikçi
he is a cleaner
he/she is a police
o bir polis
o bir asker
he is a soldier
she is a teacher
o bir öğretmen
o bit sekreter
he is a secretary
he is a friend
o bit arkadaş
o bir sevgili
she is a lover
she does not like her
onu sevmiyor
onu seviyor
she loves him
onu görüyor
onu göremiyor
she sees it
he cannot see him
o onu kucakliyor
she is embracing her
o onu kucaklamiyor
he does not embrace it
o evli
she is married
o bekar
he is single
o mutlu
he’s happy
o mutsuz
she is unhappy
o çalişkan
he is hard working
o tembel
she is lazy
https://twitter.com/seyyedreza/status/935291317252493312
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