|
The
obscenity of Elon
Musk becoming a
trillionaire and the
insane bubble
financing that is
forcing us all to be
AI’s bitches
led me back to a
book that I am sure
has been
underdiscussed but
is essential for
processing the
coming debacle AI.
I have read Resisting
AI: An Anti-fascist
Approach to
Artificial
Intelligence by Dan McQuillan twice – once to mark it up and once again just to read it unencumbered by pens and marginalia – because McQuillan has packed it with so much information distilled into sharp phrasings and lucid explanations. It probably deserves a third read as well.
He begins by
explaining how the
machine learning
that powers AI
operates. I do not
understand the math
or coding that
shapes AI (though
Broussard in How Computers Misunderstand the World gives a good illustration of how the process works), but it operates essentially as a distillery. Just as a distiller abstracts out alcohol from a complex batch of ingredients – discarding and discarding and discarding until he gets the one product that he wants – the operations of AI extract from a mess of messy data predictions based on probabilities and correlations and patterns. (McQuillan: “Any AI-like system will act as a condenser for existing forms of structural and cultural violence.”)
Because it involves
math and appears to
be scientific
(though AI and its
algorithms are not
scientific at all,
as McQuillan points
in his section on
“Scientism”
[47-51]), people
assume that the
results are free
from bias and speak
the truth about
whatever task the
program has been
asked to solve
– that the
program has been
effective. The
results are
therefore supposedly
more trustworthy
than judgments and
considerations
offered by actual
human beings.
But as McQuillan
points out time and
time again, this
conclusion is
completely false.
First, the program
can only use the
data fed into it to
do its work, and if
those data are
biased, then the
results will be
biased. He cites
well-known stories
about how facial
recognition often
fails to identify
black or brown faces
because of the
overabundance of
white faces in the
training data or how
COMPAS, the
sentence-prediction
program that
supposedly helps
judges assess the
risks of people
re-offending,
consistently gives
black and brown
people higher risk
scores than white
people for the same
criminal
circumstances.
Second, algorithms
cannot be divorced
from the social,
political and
economic conditions
in which they are
forged. The
algorithms are
created to serve
institutional
purposes, and those
purposes are
grounded in long
histories of
exploitation,
colonialism and
violence of all
kinds –
because of this,
their training data
can never be free
from the past, and
the algorithms are
doomed to
recapitulate it. (In
other words, AI can
never imagine a
future outside of
the futures
predicated on the
pasts included in
its training data.)
McQuillan cites many
instances of what he
calls
“algorithmic
violence”
where AI programs
increase, as a
matter of policy,
the
“othering”
of certain parts of
society, which
literally decides
who will live and
who will die (what
he and others call
“necropolitics”).
This can be done
through increasing
precarity (think of
Uber drivers and
others in the gig
economy),
racialization
(turning slight
differences among
humans into
essentialized
groupings),
incarceration (and
the “carceral
state,” with
fantasies of
predictive
policing), eugenics
and race
“science”
– the list of
operations is long
and dismal.
Third, what gives
the algorithmic
violence the
sanction to do what
it does is what
McQuillan calls the
creation of
“states of
exception”: a
social and political
state of being where
the law establishes
spaces and practices
not bound by the law
in order to achieve
certain ends,
usually dealing with
a nation’s
“security,”
such as treating
immigrants as
invasive hordes or
creating black sites
where prohibitions
against torture have
no force. (The
paradox, as
McQuillan points
out, of the law
using itself to
nullify its duty to
provide a bulwark
against lawlessness.)
All these aspects of
AI make it a
technology easily
adapted to a fascist
politics, a politics
that seems to be
thriving in certain
parts of the world
(including the
United States).
McQuillan cites a
phrase by Roger
Griffin,
“palingenetic
ultranationalism,”
that sums up the
fascist ideology:
The palingenetic bit
simply means
national rebirth;
that the nation
needs to be reborn
from some kind of
current decadence
and reclaim its
glorious past, a
process which will
inevitably be
violent. The term
ultranationalism
indicates that
we’re not
talking about a
nation defined by
citizenship but by
organic membership
of an ethnic
community. Hence,
with AI, we should
be watchful for
functionality that
contributes to
violent separations
of ‘us and
them’,
especially those
that seem to
essentialize
differences. [using
British punctuation]
Similar
undercurrents can be
found in “Make
America Great
Again.”
What is to be done?
His last three
chapters contain
strategies about
containing AI, and
much of what he
talks about –
the commons and
“commoning,”
mutual aid,
horizontal
decision-making,
peoples’ and
workers’
councils –
reminded me of David
Graeber’s
discussion of what
Occupy tried to
tutor the American
people about. He
also includes
fascinating
discussions of
feminist standpoint
theory and feminist
new materialism,
post-normal science
– I have never
heard of these
concepts; reading
about them refreshed
and unhinged (in a
good way) my
critical stance.
His view of a world
of mutual aid and
attention paid to
care and a new
“apparatus”
(a term he gives to
AI at the beginning
of the book) that
doesn’t strive
to
“solve”
anything “but
to sustain the
delivery of systems
of care and social
reproduction under
changing conditions
in ways that
contribute to
collective
emancipation”
(148) gives me a
feeling of both hope
and longing, much
like the effect of
that other book
I’m reading at
the
moment, The
Communist Manifesto
(brilliantly
explicated by China
Miéville in his A Spectre, Haunting),
a yearning for the
kingdom of heaven on
earth.
|
