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technology as a means of resistance. Others have
offered technological solutions for problems of
transparency, representation, and moral ethical
norms.1 With each of these perspectives, we agree
that there is a problem with the universal model of
technology and we want to say something about this
given the work we’ve been doing on data capitalism
and predictive intelligence, as we argue that what
these perspectives do not address is the postcolonial
epistemology of the machine.
From this standpoint, critiques of automation that
propose that machines can be programmed to do what
is socially or politically progressive fall in to the risk
of assuming that the algorithm is transparent and can
be co-opted and repurposed, rather than being a sociotechnical system that can be redesigned. For us, critical design means that we are to diagnose the function
of learning and not reduce algorithms to training.
Not only does transparency make white box
assumptions about the algorithm but it also fundamentally leaves intact the universality of learning,
namely the configuration of how to learn and thus
how to know universally determined by a particular
epistemology. More specifically, the claim for transparency risks falling back into one model of knowledge, based on the given specific articulation of a
universal fairness. If you train the algorithm to be
fair and fair for everyone, this relies on communities
to argue that every community needs to have access
and fair representation, but in fact what will be
imposed is merely another universal particular, a
know-how that comes from specific views of the
world. By arguing that we need to make the algorithm fair for everyone, we are also posing another
reiteration of identity politics and not a challenge of
epistemology. This claim therefore suits precisely
the model of universal reason based on the self-positing judgment that distinguishes transparency from
opacity. We claim that this is precisely what data
capitalism desires.
Drawing from Mark Fisher’s (2009) science fiction capital, we posit that data capitalism doesn’t rely
on data as a given, but on what data can become; it
operates in the future as much as the calculation of
probabilities coincides with the predictive extraction
of surplus value. There is no given proof of one truth
in data capitalism, its operations rather require the
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search mode of induction, namely the operations of
trial and error. Here, data is completely malleable.
Because of this heuristic model that data capitalism
has, its ontology is based not on universal forms, but
is open to process. From this standpoint, it is not possible to talk about computational empiricism insofar
as the operations of induction merely anticipate and
affectively shape future events. As in Brian Massumi’s
(2015) ontopower, process can be understood both in
terms of process of becoming and epistemological
processing of information. Here, ontopower entails a
potentiation of value from and through machines,
which are there to grant the recursive reconfiguration
of the transparency principle, namely the project of
the self-determining subject to hide its over-determining subject position (da Silva, 2007). Transparency
is therefore a strategy for preserving the post-Enlightenment subject as this continues to impart the hierarchies of gender, race, and class onto a conception of
the human that has come to haunt the machine. From
this standpoint, what is claimed to be opaque in the
black box of machine learning algorithms cannot be
disentangled from the normative apparatus that
reproduces the transparency of the self-determining
subject. As cybernetic networks impose on the social
body an infrastructure of seamless communication
based on the equivalence of connections, the heuristic medium of governance withdraws into the background, out of sight in the recursive colonialism of
the transparent subject. Thus, it is the transparent
subject that goes without being challenged in the general debates on AI ethics that push for transparency or
the repurposing of technology.
What is disregarded in the dominant discourse in
AI ethics is what the “learning” is of machine learning. We want to make a few remarks on predictive
intelligence, but first want to turn to Sylvia Wynter
to discuss the importance of what can be called
“sociogenic prediction.” Far beyond the algorithmic
mis-recognition of skin color, the machine learning
of the flesh inherits Western histories of Man that
enter the constitution of new assemblages in a system of sociopolitical relations. As first coined by
Fanon (1967), the sociogenic principle is a concept
that Sylvia Wynter (2001, 2007) further developed
as a way to account for how the sociopolitical
becomes flesh. For Wynter, the sociogenic principle
Dixon-Román and Parisi
is an ontological account of how the sociopolitical
assemblages of Man and the logic of symbolic “difference” become programmed in the body resulting
into an ontogenic formation of identity that come to
brand the flesh. This sociopolitical assemblage of
Man, what Wynter also calls Western Man, has gone
through a process of auto-determinations derived
from the cosmogonies of human origin. Wynter
argues that the current iteration of cosmogony corresponds to a biohumanist homo oeconomicus, constituted by the economic theories of Adam Smith.
Here, the correlation between biological and economic survival, through the forces of selection and
optimization of survival, defines the epistemological
explanation of who is and who is not successful as a
species. It is this correlation that consolidates the
formation of the sociogenic code and ensures the
reproduction of the racialization of the world. This
also corresponds to what could be called the sociopolitical constitution of Man, as a fictive (and yet
dominant) genealogy that tells the story of being
human. For Wynter understands the reproduction of
racialization in terms of autopoetic and self-regulatory practices that are imprinted within the flesh and
as such enable the ontogenic self-replication of this
originary myth. By drawing from neurobiology,
Wynter explains how symbolic “difference” materializes in terms of ontologies via neurochemical processes that produce a racialized e/affect, making the
materiality of “difference” seem natural and thus
granting a monolithic explanation of the human.
However, it is our argument that the autopoetic institution of the sociogenic code permeates not just
human ontologies but also more-than-human ontologies including the sociotechnical assemblages of
data and algorithms (Dixon-Román, 2016).
The sociogenic coding of the other as the negative marker, we claim, is necessary to the recursive
loops of the colonial enterprise, whereby the naturalization of the dyadic structure of equivalence
between man and the world (self and other) ensures
that all remains the same under the Western sun.
From this standpoint, it seems not sufficient to claim
that the transparency of the self-determining subject
must be unveiled by demanding more transparency
from the system, and for instance asking to recode a
machine learning program in the name of an equality
3
of representation. In other words, the demand for
enlarging the normalized category of the human to
include excluded differences and shed light on the
blindness of the machine does not seamlessly ensure
a political overturning of the dyadic pattern of
self-recognition.
It is already evident therefore that if ANN (artificial neural networks) will be trained to recognize nonCaucasian features and skin colors, it will do so only
by learning to extend the sociogenic commitment to
the evolutionary ground of the biological man into the
smooth machines of a technical strata. To put it in
another way, as it stands, the cybernetic regime of
immediate communication allows no possibility of
breaking away from what Sylvia Wynter calls the
autopoetic self-determination of Man, predicated
upon the negative side of the color line (Wynter 12).
The predictive intelligence of machines here becomes
a sociotechnical assemblage that contains within itself
the seeds of an ontological re-origination of a “speciated genre or Mask of being human” (13).
To further explicate Wynter’s sociogenic principle in relation to prediction, we re-work the late statistician Leo Breiman’s paradigms of prediction. In
Breiman’s articulation of the cultures of statistics, he
talks about two paradigms: data modeling and algorithmic modeling. We agree with this distinction,
and yet we aim to add another paradigm, what we
are calling “computation modeling.” We argue the
main distinction between each of these paradigms is
how error, noise, indeterminacy, or the incomputable
is accounted for.
For instance, data modeling is characterized by
the fitting of parametric statistical models (e.g. logistic or linear regression) to a sample of data of the
population. The models are evaluated based on an
analysis of the model error, residual variance, and
goodness of fit (e.g. R-squared). As an example, data
modeling might be used in the context of predicting
the risk of someone committing a violent offense
and thus falls onto the statistical strategies of predictive policing. Here, the sociogenic has multiple pathways on algorithmic institution, including the
operationalization of the variables, what predictors
are used, and what and who comprises the sample
for parameterizing the model. These are all discursive formations of sociopolitical significance.
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If data modeling is based on a known specified
parametric model that is fit to the data, then algorithmic modeling is based on an unknown model (i.e.
black box) that is determined by the data. Algorithmic
modeling is understood to include nonparametric,
nonlinear models (e.g. Support Vector Machines,
Random Forest, or Neural Nets) that are designed to
optimize predictive accuracy; thus, the algorithm is
determined by the data, which is not a priori to data
processing. Models are evaluated based on prediction accuracy and, as such, an analysis of the error in
prediction. Apart from Breiman, our characterization
of algorithmic modeling is based on nonautomated
processes of model updating. Our contention is that
Breiman’s conception of algorithmic modeling is not
enough to account for the more profound historical
transformation of automation itself, what we characterize as computational modeling below. Applying
the above predictive policing example to algorithmic
modeling results in other patterns of sociogenic violence. With this paradigm of prediction, the sociogenic institution of the algorithm entails how the
response variable is operationalized as well as algorithm programming decisions of cost ratio of false
negatives to false positives, the determined patterns
learned from the data assemblages, and the performative enactments from feedback loops.
While algorithmic modeling is determined by the
data and designed to make more accurate predictions
than data modeling, the process of handling indeterminacy or the incomputable is distinct from what we
are calling computational modeling. Similarly, while
the type of algorithms used in computational modeling do not necessarily differ from those used in
algorithmic modeling, there is a minor yet significant difference in deployment that we argue has substantial implications for algorithmic reason and the
sociogenic. When the process of algorithmic re-estimation (i.e. learning) is automated, and therefore is
left to run the information according to the capacity
of the output to overwrite the input, introduces temporality in the algorithmic procedure of re-estimation. Temporality, for instance, is at the core of an
automated prediction model that must account for
the nonlinear and recursive loops between inputs and
outputs. The limit of the finite algorithmic model to
account for infinities already sets up the conditions
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for a reflexive function in the algorithm. The automation of this process enables the prehension of
incomputables and forms of thought that are immanent to the algorithm (Parisi, 2013). While the nonautomated process of model re-estimation accounts
for the incomputable, it does it in a way that simply
adds new data to the existing training data to estimate the algorithmic model. Instead, at the core of
computational modeling, there is an ongoing iterable
process that continues to build the model based on
algorithmic randomness without returning to the
original condition.
Employing computational modeling to discuss the
predictive policing example will point to similar
pathways that concern sociogenic violence, but with
a force of alterity. Similar to algorithmic modeling,
the sociogenic in-formations of the algorithm via the
computational modeling paradigm of prediction is
similar: the operationalizing of the response variable,
algorithm programming decisions of cost ratio of
false negatives to false positives, the determined patterns learned from the data assemblages, and the performative enactments from feedback loops. Under
computational modeling, data assemblages and feedback loops are most determinative, as the automated
learning of the algorithm is determined from patterns
in the data and the incomputable. Thus, while algorithmic modeling engages in performative acts of
prediction based on a model that was calibrated at
one point in time, computational modeling will continue to automatically re-calibrate estimated parameters based on the processing of new data. This means
that the sociogenic constitution of the algorithm is
likely to shift or reconfigure as patterns of policing
and public behaviors change. While the sociogenic
may have similar pathways for algorithmic constitution, it is the incomputable and its prehension over
time that creates conditions for alternative configurations of algorithmic thought (Parisi, 2013).
Incomputable probabilities are discrete states of
nondenumerable infinities that are incompressible to
the finite systems of algorithms. It is the halting probability of a universal free-prefix self-delimiting Turing
machine, what mathematician and information theorist Gregory Chaitin (2005) calls Omega. Its binary
expansion is an algorithmic random sequence, which
is incomputable. The incomputable is that which is
Dixon-Román and Parisi
not outside the system, it is fundamentally a part of
the system. It indicates that the system is an incomplete model of cognition.
Hence, computational modeling is used not simply to build profiles based on pre-fixed sets of algorithms, but to exploit the self-delimiting power of
computation, defined by its in/capacity to decide
when a program should stop. By transforming nondenumerable infinities into random discrete sets or
Omega probabilities, computational modeling manifests random actualities.
When automated procedures become temporal
operators of variations and heuristic searchers of
results, automation itself—that is the computational
procedure—becomes open to the indeterminacy of
its own function. In particular, algorithmic iterations, it has been argued, have become opened to the
circular looping of time. If we are to draw on more
challenging conceptions of automation, inspired for
instance by Gilbert Simondon’s (2017) general theorization of the modes of existence of technical
objects, our argument for a dynamic view of automation can suggest that temporal processing in
automation radically challenges the reproduction of
the sociogenic principle in systems of prediction.
Simondon (2017) insisted that machine design
includes a principle of indetermination, which is to
be added to the space of indeterminacy in the
human–machine relation. In particular, as Yuk Hui
(2019) has recently suggested, there is a possibility
of approaching this question of the inorganic time of
the machine in terms of recursivity. It is precisely
this link between the inorganic time and the inorganic thought of the machine that can allow us to
discuss machine thinking away from either an optimization of mechanical functions or simply as an
extension of the soul of man.
In particular, Hui (2019) takes Simondon’s proposition of a non-Cartesian form of cognition to
challenge the assumption that thinking follows a
linear chain of causes and effects, namely where
reasoning is confined to a procedure for transporting evidence from one point to another without
having any active function to rather change the
course of things. According to Hui (2019),
Simondon refuses Descartes rationalism by demonstrating that the cybernetic principle of feedback
5
adds a new temporal structure to thinking that is
described in terms of a spiral. As Hui (2019) further
explains, according to Simondon cybernetics
replaces the telos of thought with a self-regulatory
process. In particular, insofar as the recursivity of
feedback makes the cybernetic system possible, it
also impedes the system to become systematic,
complete, and simply a reproductive whole.
However, since human relations are abstracted and
re-integrated into the temporality of machines,
which, as we have suggested so far, constitute the
engine of algorithmic governmentality, the question of temporality—and thus of recursive temporality in nonorganic machines—is still in need to be
further explored.
For Hui (2019), margins of indeterminacy not
only describe the recursive temporalities of
machines, but more importantly a recursive thinking
in machines. This remark suggests that the technical
machine is not simply a mirror of the normative
apparatus of knowledge reproduction, automation,
in terms of the temporal processing of outputs, it can
include both contingency and chance within itself
because the temporality of the technical object or
cybernetic machines precisely admits that errors,
incident, and failure are part of the causal process of
machine learning. From this standpoint, it is important to transform the conception of the algorithm
itself as being not simply pre-determined by its
default or setting position. As much as the recursivity of the system allows for a change over time, so
too it points to the indeterminate elaboration of an
“algorithm without programs.”
If, as we have seen, recursive feedback is at the
core of the re-estimation model used in statistics to
predict outcomes on the basis of a given data set or of
a training data set, then it is important to further reenvision how recursivity works through infinities,
that is how what cannot be known in advance becomes
a problem of compression or patterning of infinities.
From this standpoint, one can argue that the science of
statistics can be taken as a techno-scientific instance
of epistemological reconfigurations of the problem of
the incomputable in the formation of feedback systems of predictive intelligence. What we are arguing
therefore is that assumptions of technological determinism that we find in debates about the reproduction
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of biases in systems of predictive intelligence has
nothing to do with the technical machine, but is rather
the result of a continuous re-territorialization of the
techno-social possibilities of re-inventing epistemological paradigms outside the framework of colonial
capital. From this standpoint, data capitalism takes
recursive accumulation in statistics to preserve human
capital in machines so that feedback procedures of
estimation remain anchored to the reproduction of
social relations, embedded in the material-historical
determination of man. In other words, what is deterministic in statistical procedures of estimation is not
the cybernetic principles of feedback or computational patterning, but above all the matrix of representation, where the sociogenic principles are amplified
and distributed through the image of the automata that
must maintain an objective efficiency.
From this standpoint, incomputables are not simply open to be co-opted because they already constitute or structure the system to define a new order of
cosmogony admitting the indeterminacy of knowledge as an inevitable consequence of computational
know-hows. This proposition is only one possibility
for continuing to question—debunk and construct
anew—the implications of techno-scientific epistemology in matters of governance, one that points
toward a necessary transformative force or the counter-futures of AI ethics.
Eubanks, V. (2017). Automating inequality: How hightech tools profile, police, and punish the poor. St.
Martin’s Press.
Fanon, F. (1967). Black skin, white masks. Grove Press.
Fisher, M. (2009). Capitalist realism: Is there no alternative? Zero Books.
Hui, Y. (2019). Recursivity and contingency. Rowman &
Littlefield International.
Massumi, B. (2015). Ontopower: Wars, powers, and the
state of perception. Duke University Press.
Noble, S. (2018). Algorithms of oppression: How search
engines reinforce racism. New York University Press.
Parisi, L. (2013). Contagious architecture: Computation,
aesthetics, and space. MIT Press.
Roth, A., & Kearn, M. (2019). The ethical algorithm: The
science of socially aware algorithm design. Oxford
University Press.
Simondon, G. (2017). The genesis of technicity. E-flux
Journal, 82, 1–15.
Sloane, M. (2019). Co-opting AI article series. Public
Books. https://www.publicbooks.org/co-opting-ai/
Wynter, S. (2001). Towards the sociogenic principle: Fanon,
identity, the puzzle of conscious experience, and what it
is like to be Black. In A. Gomez-Moriana & M. DuranCogan (Eds.), National identities and sociopolitical
changes in Latin America (pp. 30–66). Routledge.
Wynter, S. (2007). Human being as noun? Or being human
as praxis? Towards the autopoetic turn/overturn: A
manifesto. https://s3.amazonaws.com/arena-attachmen
ts/1516556/69a8a25c597f33bf66af6cdf411d58c2.pdf
Note
Author biographies
1.
Ezekiel Dixon-Román is an Associate Professor in the
School of Social Policy & Practice at the University of
Pennsylvania. His interdisciplinary scholarship is focused on
the cultural studies of quantification and critical theories of
difference. He is the author of Inheriting Possibility: Social
Reproduction & Quantification in Education (2017,
University of Minnesota Press) and is currently working on
a book project that examines the haunting formations of the
transparent subject in algorithmic governance and the potential transformative technopolitical ontoepistemologies.
In these general debates on artificial intelligence (AI)
and ethics, we are thinking of many of the data policies in algorithmic governance, much of the FAT*
(Fairness, Accountability, Transparency) discourse
on ethics in AI, Mona Sloane’s (2019) Co-opting
AI article series in Public Books, Safiya Noble’s
(2018) Algorithms of Oppression, Virginia Eubanks’s
(2017) Automating Inequality, and Aaron Roth’s and
Michael Kearn’s (2019) The Ethical Algorithm, just
to mention a few examples.
References
Chaitin, G. (2005). Meta math! The quest for omega.
Vintage.
da Silva, D. F. (2007). Toward a global idea of race.
University of Minnesota Press.
Dixon-Román, E. (2016). Algo-ritmo: More-than-human
performative acts and the racializing assemblages of
algorithmic architectures. Cultural Studies-Critical
Methodologies, 16(5), 482–490.
Luciana Parisi’s research is a philosophical investigation of
technology in culture, aesthetics and politics. She is a
Professor of Media Philosophy at the Program in Literature
and the Computational Media Art and Culture at Duke
University. She is the author of Abstract Sex: Philosophy,
Biotechnology and the Mutations of Desire (2004, Continuum
Press) and Contagious Architecture. Computation, Aesthetics
and Space (2013, MIT Press). She is currently completing a
monograph on alien epistemologies and the transformation of
logical thinking in computation.