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

is trained on data sets. It is this training that
AI
what AI “knows”. The training data
determines
often assumed to be objective, ahistorical and
is
but that assumption is incorrect.
non-ideological,
data may consist of images that are
Training
sorted and labelled by a group of
selected,
usually men from relatively privileged
people,
working and living in contexts
backgrounds,
Joanna Bryson at the UK’s University of Bath
Dr
of computer science remarks that
department
are programmed by “white, single
machines
from California” and that diversifying the
guys
might help. Bryson adds: “There is no
workforce
way to create fairness. Bias is not a
mathematical
word in machine learning. It just means that
bad
machine is picking up regularities. It is up to
the
to decide what those regularities should be”
us
in Santamicone 2019).
(quoted
people of color, women, the disabled,
“As
and other vulnerable communities
LGBTQ+,
impacted by data-centric
disproportionately
we must find tangible ways to
technologies,
ourselves into the creation, training,
insert
testing of algorithmic matrices …
and
systems are encoded with the same
These
responsible for the myriad systemic
biases
we experience today. We can no
injustices
afford to be passive consumers or
longer
subjects to algorithmic systems that
oblivious
impact how and where welive,
significantly
we love and our ability to buildand
who
wealth.” (Dinkins, undated)
distribute
has been made on this front.
Progress
Institute for Human-Centered
Stanford’s
Intelligence (HAI) has a mission
Artificial
recruit designers who are: “broadly
to
of humanity … across gender,
representative
nationality, culture and age, as well as
ethnicity,
this is not just a technology problem,
Ultimately,
a political one that we encounter in many
but
spheres of life. It raises the question
different
whether technology will continue to mirror
of
rather than emancipate it. Jill Lepore,
humanity,
historian of polling at Harvard University, has
a
that data science enables data consultants
argued
dictate politicians’ views, and not the other
to
around: “data science is the solution to one
way
but the amplification of a much bigger
problem
political problem” (Lepore quoted in
one—the
2016).
Wood
should also be concerned with the question
We
responsibility: if an algorithm does, indeed,
of
out to make racist or sexist or otherwise
turn
judgments, do we hold its creators
unethical
Project al-Khwarizmi, Stephanie Dinkins seeks
In
empower communities of colour to participate
to
knowledge production and application in
in
Follow the link to find out more
technology.
terms: Dinkins Project
Search
al-Khwarizmi
Which kinds of knowledge are
1.
exchanged between the computer
being
How does this project influence your
2.
on who should be involved in the
opinion
In what ways should the processes of
3.
and applying knowledge ensure
producing
3IV. Ethics
dissimilar to those of most human beings.
can’t aord to have a tech that is run by
We
exclusive and homogenous group creating
an
that impacts us all. We need more
technology
about people, like human psychology,
experts
and history. AI needs more unlikely people.
behavior
2018)
(Thomas
discussion
For
across disciplines”.
accountable?
AI and algorithms through an antioppression
lens
about her work, then consider the questions.
scientists and the community participants?
production of technological knowledge?
that AI is more just and socially equitable?
86