Data Science as Machinic Neoplatonism
Received: 22 October 2016 / Accepted: 2 August 2017 / Published online: 21 August 2017
#The Author(s) 2017. This article is an open access publication
Abstract Data science is not simply a method but an organising idea. Commitment to
the new paradigm overrides concerns caused by collateral damage, and only a coun-
terculture can constitute an effective critique. Understanding data science requires an
appreciation of what algorithms actually do; in particular, how machine learning learns.
The resulting ‘insight through opacity’drives the observable problems of algorithmic
discrimination and the evasion of due process. But attempts to stem the tide have not
grasped the nature of data science as both metaphysical and machinic. Data science
strongly echoes the neoplatonism that informed the early science of Copernicus and
Galileo. It appears to reveal a hidden mathematical order in the world that is superior to
our direct experience. The new symmetry of these orderings is more compelling than
the actual results. Data science does not only make possible a new way of knowing but
acts directly on it; by converting predictions to pre-emptions, it becomes a machinic
metaphysics. The people enrolled in this apparatus risk an abstraction of accountability
and the production of ‘thoughtlessness’. Susceptibility to data science can be contested
through critiques of science, especially standpoint theory, which opposes the ‘view
from nowhere’without abandoning the empirical methods. But a counterculture of data
science must be material as well as discursive. Karen Barad’s idea of agential realism
can reconfigure data science to produce both non-dualistic philosophy and participatory
agency. An example of relevant praxis points to the real possibility of ‘machine
learning for the people’.
Keywords Machine learning .Algorithms .Data science .Counterculture .Standpoint
theory.Agential realism .Neo-platonism .Big data .Participation .Agency
Philos. Technol. (2018) 31:253–272
Department of Computing, Goldsmiths, University of London, London, UK
1 Data Science as Organising Idea
Data science is not simply a method but an organising idea. That is, an underlying
shift in perspective and practices of the kind that Kuhn called a paradigm (Kuhn
1996). This should be appreciated when trying to critique data science based on
the occurrence of collateral damage. The commitment to adopting data science in
more and more areas of life will not be constrained by limits such as data
protection, because it is based on a new normativity. Only a countercultural
critique can dissect the conditions that constitute the possibility of data science
and propose an alternative.
To start the process of developing this counterculture, it is necessary to under-
stand what data science actually does. The surge of popular interest in big data has
also generated misinformation about its core concepts and practices. To grasp the
essence of data science means examining the algorithmic methods that make data
science possible, in particular, forms of machine learning. Shedding light on the
practical strengths and weaknesses of data science allows us to illuminate real
concerns about its operations in the world, ranging from algorithmic discrimina-
tion to the evasion of legal due process. But we cannot leave it at that. As Kuhn
made clear, contrary to the assumptions of naive empiricism, reality is not directly
accessible to us as ‘facts’that can be recorded by suitable devices and rendered as
theory. While we can access reality, we cannot do so without some level of
meaning making. What we sense, through whatever device, already has meaning
to us, and meaning is not an object of sensory perception. What is also present is a
pattern of cognition which enables the ‘seeing’. As an organising idea, data
science does not simply reorganise facts but transforms them.
Data science can be understood as an echo of the neo-platonism that informed
early modern science in the work of Copernicus and Galileo. That is, it resonates
with a belief in a hidden mathematical order that is ontologically superior to the
one available to our everyday senses. Looking at how this defines the character
of data science provides a skeleton key to understanding its likely consequences.
It also helps explain the widespread commitment to data science in the face of
actual results that fall far short of the promulgated vision. When characterising
data science philosophically as a form of neo-platonism, it is important to
understand the difference between data science and discursive philosophy. Data
science does not affect by argument alone but acts directly in the world as a
form of algorithmic force. It is machinic, that is, an assembly of flows and logic
that enrolls humans and technology in a larger, purposeful structure. While
algorithms and data are the bone and sinew of data science, its vital force comes
from general computation. As computation becomes pervasive, capturing and
reorganising human activity, data science exerts its philosophy directly as order-
ings, decisions and outcomes.
2 What is Data Science?
There is considerable variety in the way practitioners themselves describe data
science. It is a lively term that functions both as a flag of convenience for statistics
254 McQuillan D.
and a genuinely new discipline (Quora 2014). As the latter, it embraces a grab bag
of skills including programming (typically in R or Python), data munging (pars-
ing, scraping and formatting data), statistics, linear algebra, multivariate calculus,
SQL (structured query language), machine learning and data visualisation (Holtz
2014). The imaginary ideal data scientist is a Renaissance figure with a mastery of
all these arts. The point of overlap of these skills occupied by any particular data
scientist will usually reflect their personal background and the role they are
recruited for. Typical routes into data science include someone with a background
in statistics who learns to code, or someone strong in programming who has
acquired an appreciation of analytical modelling and problem-solving. Hence,
the much-retweeted saying: ‘Data Scientist (n.): Person who is better at statistics
than any software engineer and better at software engineering than any statisti-
cian.’(Wills 2012). The definition of data science as a practice is also malleable
and is strongly shaped by context, which could be coding operational systems to
claw basic metrics from a rising flood of data, or creating new data-driven
products using sophisticated machine learning techniques at a level similar to
academic research (O’Neil and Schutt 2013). In this swirl of recruitment and
entrepreneurial pivoting, it can be hard to discern why data science has become
so important, so quickly. The key dynamic is the encounter between the contem-
porary data flood and the forms of computation that can transform it into action-
The most important methods that have been called forth by the presence of plentiful
data and cheap computing infrastructure can be grouped under the heading of machine
learning. These methods thrive on the volume and variety of their input and can thus be
made to wrest meanings from big data. They do so by assuming a functional relation-
ship between the input features, which can be any number of different measurable or
categorisable aspects of the context under study, and the desired output, which can be
anything from a prediction of future house prices to the likelihood of a tumour being
malignant. Supervised machine learning algorithms are trained on data sets where the
outcome is already known. During the process of training, the algorithm tries to force a
fit between the selected features of the input data and the known output by varying the
parameters of the assumed relationship. The forcing is mathematical, calculating an
algorithmic ‘cost’for the distance between the fit and the data.
For example, the method of logistic regression tries to find a boundary between two
sets of input data, let us say between features that correlate with malignant or benign
tumours. The set of features is selected from the available clinical data, along with the
known diagnoses. The task of the algorithm is to find a decision boundary between the
data for the cancerous tumours and the non-cancerous ones. If we imagine that there are
only two key features (e.g. age and length of tumour) and these are used to plot the data
on an x-ygraph, the set of training data can be visualised as a cluster of green dots
(benign) and a cluster of red dots (malignant) which intermingle a bit where the clusters
overlap. The decision boundary would be a line that can be plausibly drawn between
the points representing malignant tumours and the points representing benign tumours,
such that the vast majority in each case fall clearly on one or other side of the line. (In
reality, most machine learning involves a larger set of features and the vector space of
features is multidimensional, so there is no easy way to visualise what is going on.).
The boundary is created by assuming that the probability of a set of features mapping to
Data Science as Machinic Neoplatonism 255
one of the outcomes takes the form of a sigmoid function,
that is, one that pushes
minor differences quickly towards the asymptotic values of one or zero (malignant or
benign) (Ng 2012). This is mathematically reasonable, but it is important to understand
that it is not based on a causal or physical understanding of tumours. It is simply
intended to force a mathematical fit.
The overall cost for a set of input data is calculated from all the individual
differences between the actual data points and where they ‘should’be according to
the function being fitted. Iterating over this process finds a minimum cost for the fitted
features, i.e. the compromise that best fits the predictions to the actual outcomes. The
calculated cost is not a function of the input features but of parameters of the fit, that is,
of the relative weights of the different features, so it is in effect deciding which of the
features is important, and by how much. Having ‘learned’how to discern the two kinds
of data by processing thousands of known cases, the algorithm can make a rapid
judgement on any future data by generating a prediction about whether that case is
malignant or benign. In practice, most applications of machine learning will not restrict
themselves to a couple of input features. Machine learning algorithms can work with
hundreds or even thousands of different features, so they are well suited to a world
where huge amounts of heterogeneous digital data have become available. However,
the scale and complexity of the calculations also means that the algorithmic decision
about which features are important is not necessarily reversible to human reasoning.
While in some cases, it will be obvious why the algorithm picks a certain ratio of
features, in other cases the algorithm’s‘reasoning’will be obscure. This obscurity is
intensified in the case of neural networks.
Neural network algorithms are a modified form of machine learning that are
becoming increasingly important (Hastie et al. 2003). They are suited to forms of input
data that are hard to parameterise and complex to fit. For example, faces or handwritten
letters come in many different forms; while humans learn from an early age to
recognise them, it is tricky to write a specification that is precise enough for a machine
yet flexible enough to deal with all the natural variations. The structure of a neural
network applies the same starter logic as the logistic regression described earlier. There
is a set of inputs, called nodes rather than features in this case, and a set of starting
parameters which are intended to map the input nodes onto the target output. The
difference is that this mapping goes via an additional hidden layer of nodes (Skymind
2016). Each of the initial nodes is mapped to each of the nodes in the hidden layer, and
in turn, the hidden layer is mapped to the target (the desired outcome classification).
The overall effect can be thought of as the hidden layer enabling the neural network to
distil its own set of features, which it then uses to discriminate between ‘face’and ‘not
face’, or between letters or numbers. Given a large enough training set, the neural
A sigmoid function is a mathematical function taking the form of a slanted ‘S’shape. The middle of the S
crosses the axis at (x=0,y= 0.5). For positive x, the top of the S quickly approaches one, which it finally
reaches at infinity (i.e. asymptotically). Likewise, for negative x, the bottom of the S rapidly approaches zero,
reaching it asymptotically at minus infinity. The effect of this slanted and stretched S shape is that as x
increases slightly beyond zero, the corresponding value of y(which is a probability in our case) slides quickly
towards one, and as xdecreases slightly below zero, the value of ysinks quickly to zero. Thus, it tends to
eliminate any ambiguous middle ground; for the vast majority of x,y(the probability) will either be effectively
one or zero. The point here is that using the sigmoid function is a choice. It makes the machine learning
effective, but it is not mandated by scientific causality.
256 McQuillan D.
network abstracts its own set of hidden features, which can be very effective for
complex and messy input data.
But the nature of these features is also hidden from the operators. By definition, no
human software engineer defines what these abstracted features are, and even if the
contents of the hidden layer are examined, it is not necessarily possible to translate that
back into comprehensible reasoning. In an animation of an early example of a neural
network learning to recognise handwritten numerals, we can see recognisable though
blurred representations of the numbers in the input layer (LeCun 1998). But, the
contents of the hidden layer look like some kind of condensed barcode. Why this
particular weighting? Why is this particular set of subtle combinatorics applied to the
input data? We cannot necessarily tell. All we can say for sure is that, in many cases, it
is surprisingly effective. Moreover, when we are talking about deep neural networks,
where there are multiple hidden layers, the capacity for drawing predictions from messy
data can be uncanny (Karpathy and Fei-Fei 2015). Deep neural networks are compu-
tationally demanding, but they have recently taken off because of a combination of
supply and demand; the cheap distributed computing power has become available, and
the need for methods that can handle huge levels of messy data has become urgent.
Neural networks are emblematic of the machine learning’s tendency to provide ‘insight
through opacity’. The opacity of machine learning is not only that of the black box. It is
also a consequence of algorithms hidden behind the high walls of commercial secrecy.
But it is also because they have a tendency to be opaque by nature.
3 The Problem with Data Science
Machine learning wrests apparent meaning from the streams of data that are the
inevitable consequence of current digital conditions. It forces functional fits to draw
tenuous connections between different phenomena that are otherwise inaccessible to
human apprehension. Thus, data science appears to emulate science by transforming
empirical data into patterns of regularity which have predictive power. In context, such
as in a carefully parameterised support tool for clinical decisions, the abstracted insights
of machine learning can add a lot of value. But the production of actionable results is
deeply seductive, especially in contexts where risk or profit is at stake. Several
problems have become apparent as early adopters rush to implement data-driven
policies across the board. Some drawbacks are obvious; for example, data science
and machine learning cannot transform bad input into good output. If the data itself
carries embedded social bias or prejudice, then so will the nominally neutral output of
the algorithms. This has become evident in predictive policing and parole software in
the US criminal justice system, where there is an enthusiastic application of data
science but where the underlying data reflects deeply rooted racial issues. A recent
analysis of a recidivism algorithm used in sentencing in some US courts showed racial
disparities in the predictions of future reoffending (Angwin et al. 2016).
The less obvious problem here is the potential production of new forms of
unrecognised prejudice. The whole point of data science is the analysis of a scale
and complexity that is beyond direct comprehension by the people. Whereas critical
observers are becoming alert to the machinic production of prejudice along race or
gender lines (Social Media Collective 2016), the distant and multidimensional nature of
Data Science as Machinic Neoplatonism 257
the machine learning’s correlations may mean that subtler forms of discrimination go
unnoticed. The algorithms may settle on a certain combination of features as an
identifier that effectively minimises their cost functions, which amount to a real-
world segmentation that no one expected or, given the scale of the data and the
complexity of the algorithms, actually notices (McQuillan 2015). As the pace of data
science adoption far outstrips the evolution of law and regulation, it is hard to know
who will ensure fair play when, for example, ‘Predikt’s AI finds you candidates similar
to your best hires’through an ‘algorithmically curated pool of active and passive talent’
(Predikt Inc 2016). Considering the way a deep convolutional neural network is trained
for facial recognition, a cheerful introductory article aimed at practitioners remarks ‘So
what parts of the face are these 128 numbers measuring exactly? It turns out that we
have no idea. It doesn’t really matter to us. All that we care is that the network generates
nearly the same numbers when looking at two different pictures of the same person’
(Geitgey 2016). It may indeed be the case that metrics that seem important to humans,
such as eye colour, are not that helpful to a computer analysing a histogram of pixel
gradients and that deep learning does a better job than humans in figuring out which
parts of the face to measure for machine learning. But what happens when the same,
seemingly objective methodologies are carried over to the legal context? If the process
of making these predictions is inherently opaque to human reasoning, we are
abandoning the basic principle of due process. As an article in the Stanford Law
Review put it, ‘big data’s power to enable a dangerous new philosophy of pre-emption’
means ‘the justification for a fundamental jurisprudential shift from our current ex post
facto system of penalties and punishments to ex ante preventative measures that are
increasingly being adopted across various sectors of society’(Earle and Kerr 2013).
The potential is created for discrimination that evades due process. Moreover, the
classifications of predictive algorithms may themselves change the people’sbehaviour
in ways that the model did not learn about when it was trained (Mackenzie 2015)
leading to a recursive reinforcement of the machine learning model as actual social
practice (McQuillan 2016).
The problems of insight through opacity do not only occur at the level of individual
applications. For some years, Silicon Valley luminaries have promoted ideas like
algorithmic regulation, where social problems that drive aspects of government
rulemaking are treated on a par with malware and spam (Howard 2012). According
to Tim O’Reilly, for example, the idea of algorithmic regulation is core to the
functioning of all internet platforms and should act as an inspiration for the design of
a twenty-first century government. He valorises the data-driven automaticity of these
systems over the alleged inefficacy of policy-making. As he puts it in a talk to the Long
Now Foundation: ‘If you look at, say, the way spam is regulated on the Internet, that’s
the beginnings of a kind of an immune system response to a pathogen and works a lot
like biology: you recognise the signature of something new and hostile and you fix it....
You compare that to how government regulation works, and you go: ‘It’sjustbadly
broken!’Somebody puts out some rules, and there’s no method of enforcement’
(Morozov 2013). Spam filter software, as a prosaic and practical application of machine
learning, may decide to bin emails based on obscure combinations of apparently
innocuous terms rather than using the clues that stand out to us, such as subject lines
that shout about the chance to win millions of dollars (Burrell 2016). We might be
prepared to tolerate some false positive from our spam filters, where a genuine email
258 McQuillan D.
ends up classified as junk. But it must be a matter for concern where a similar opacity
leaks into governmental and legal systems.
Clearly, the traditional notion of data protection is of little relevance when it comes
to data science. It is not viable to define a core set of private data when generating data
is an innate function of so many essential systems, and any apparently innocuous data
can be absorbed into the expansive correlations of the algorithms. Metadata alone is so
powerful for surveillance that the NSA do not care about the actual content of our
messages (Opsahl 2013), while logistic regression can turn Facebook friendships into
predictions of hidden sexuality (Jernigan and Mistree 2009). Nevertheless, there are
several emerging areas of activity that attempt to pre-empt a tide of bad outcomes from
data science. Some demand the opening of corporate and governmental black boxes so
that algorithms can be subject to examination, following the invocation of Judge
Brandies that ‘Sunlight is said to be the best of disinfectants’(Pasquale 2016). While
having some effect, this does nothing to address the core opacities of methods like
machine learning. Others attempt to probe the social consequences directly by porting
methods from the social sciences, like the audit study (Sandvig et al. 2014), although
this is most suited to the limited subset of algorithmic influence that presents itself
through public interfaces. Finally, there is a small but growing number of computer
scientists who are attempting to develop anti-discriminatory remedies at the level of
data and algorithms (Feldman et al. 2014; Hajian and Domingo-Ferrer 2012). While
having the merit of trying to correct data science from a perspective that understands
the technicalities of its operations, it is constrained by seeing data science as an external
set of methods rather than as a broader social apparatus in Foucault’ssense,thatis‘a
thoroughly heterogeneous ensemble consisting of discourses, institutions, architectural
forms, regulatory decisions, laws, administrative measures, scientific statements, phil-
osophical, moral and philanthropic propositions’(Foucault 1988). Humanist critiques
of big data acknowledge that coming to the world through correlation has merit if the
overall purpose is acting in the world, but point out that the cost is ‘a massive reduction
of what it means to Bknow^’(Bowker 2014). Moreover, the imaginaries that arise
alongside big data place all their analytical value on the idea of anticipation, in part,
because they are ‘shaped by positivist traditions that equate scientific value with
predictive laws’(Boellstorff 2013).
A broader framework for corrective action can be generated by seeing that data
science is in fact more than the sum of its parts; that it represents a new way of
structuring thought that draws allegiance from older historical currents and, as an
organising idea, redefines observations and norms; and that it has a social momentum
derived from both its metaphysical and machinic aspects.
These notions can be summed up by saying that data science is, or is becoming, an
automated form of applied philosophy: a machinic neoplatonism
What would it mean to say that data science is neoplatonic? The philosophical school
of platonism, as distinct from any arguments about what Plato himself did or did not
believe, is committed to a two-world metaphysics (Yates 2002). Behind the world of
the sensible, that which we experience through our senses, is the world of the Form or
Data Science as Machinic Neoplatonism 259
the Idea. Experiences are the imperfect imprint of this perfect yet inaccessible second
layer. As such, the world of the Idea is ontologically superior to the one we actually
inhabit. There is no intelligibility in the world that we encounter through sense
experience, and we can only come to true knowledge through contemplation of perfect
Forms which are eternal and unchanging. Platonism has been a strong and continuing
influence on many schools of later philosophy, so there are many forms of neoplato-
nism. For Plato and the neoplatonists, mathematics is the liminal realm between the
imperfect and transitory world of the senses and the perfect and eternal world of pure
spirit. Mathematical relations concerning triangles and circles, for example, are true
independently of any particular triangle or circle. They are properties of pure triangu-
larity or circularity which cannot be drawn as such. Yet, any triangle or circle that is
drawn must reflect them imperfectly inasmuch as they are triangular and circular. Thus,
each triangle or circle participates simultaneously both in the intelligible and the visible.
The kind of neoplatonism of most interest here emerged in the work of Copernicus
and Galileo (Kuhn 1995). As a paradigm, it shaped the development of modern science,
and it is resurfacing again in data science. Rather than being led to his beliefs by simple
empirical observation, Copernicus ‘took pains to read again the works of all the
philosophers on whom I could lay hand’(Kuhn 1995, p. 142) and was influenced by
his readingsof older neoplatonism sources that contained the idea of a moving Earth and
the central importance ofthe Sun in the universe. Copernicus was strongly influenced by
the mathematical strand of neoplatonism and believed that the true order of things is a
mathematical harmony consisting of arithmetical and geometric relationships. It is
important to appreciate the way Copernicus, by his own account, was motivated by
these geometric and mathematical symmetries. His dispute with the historically domi-
nant system of Ptolemaic astronomy was not based on empirical observations but his
perception that they had not ‘been able thereby to discern or deduce the principle
thing—namely, the shape of the Universe and the unchangeable symmetry of its parts’
(Kuhn 1995, p. 139). In the Ptolemaic system, it is assumed that the Earth is stationary
and the motion of the planets is circular, and in order to reconcile this with the observed
complexity of planetary motion, it had, over time, been necessary to add an intricate
system of epicycles and deferents. While the seven-circle system introduced early in
Copernicus’s work is both simpler and symmetric, and centred on the Sun, it is actually
inferior to the Ptolemaic system in terms of accuracy of predictions. Copernicus had to
introduce his own modifications of epicycles and eccentrics to make his version even
match the older one for accuracy (Kuhn 1995, p. 154). The point is not to look back from
our contemporary context in the knowledge that Copernicus was right, but to understand
that the commitment to the Copernican view does not represent the triumph of empirical
observation over inferior superstition. Rather, it was a commitment to carry forward a
new organising idea that arose from a philosophical standpoint, a commitment which
cannot be reduced to the scientific method as we understand it.
This commitment was shared by Galileo, and he further developed the Copernican
model. In doing so, he established some fundamental tenets of modern science. Rather
than being deterred by the counter evidence of our senses, that the motion of the Earth
according to Copernicus should be apparent through the experience of strong wind and
the fact that objects in the air would be left behind by the rotating surface, Galileo
inverted the problem. Instead of doubting the motion of the planet, he developed a new
260 McQuillan D.
physics of motion to explain the way bodies move as if the Earth was at rest; ‘the
crucial thing is being able to move the Earth without causing a thousand inconve-
niences’(Galileo 2016). His basic idea can be described as ‘indifference’,thatis,a
body is indifferent to its state of motion in general. If a body is indifferent to its state of
motion, it can have several motions at the same time without them interfering with each
other. In other words, the net motion can be seen as the superposition of analytical
components. Thus, all bodies have the state of motion of the Earth while appearing to
us the same as if the Earth was at rest. The idea that movement was a state was a radical
shift from the older idea that motion was something involving the essential nature of the
body; a necessary feature, a ‘becoming’of the body itself. It laid the foundations for the
later idea of inertial motion and the physics of Newton. This breakthrough was founded
on a belief that truth could be discerned by going against the direct experience of the
senses. The metaphysics that Copernicus and Galileo bequeathed to science was a
belief in hidden layer of reality which is ontologically superior, expressed mathemat-
ically and apprehended by going against direct experience.
5 Neoplatonic Data Science
As a method for revealing a hidden mathematical order in the world, data science
strongly echoes this neoplatonic project. For the data scientist, computation plays the
role of the intermediary between the imperfect world of data and the pure function that
relates the features to the target. While the scientific project required a mathematisation
of the world, data science requires the datafication of the world. The scientific
requirement that empirical facts must be measurable led to the division of qualities
into primary and secondary. Primary qualities such as number, magnitude, position and
extension can be expressed mathematically, whereas aspects which seem to us an
inseparable part of phenomena are relegated to secondary qualities, mere sensory
echoes. Hence, Newton replaced colour with ‘degrees of refrangibility’. For data
science, the primary qualities are those that can be expressed as data. Rather than
drawing on the first person view of reality, it follows the scientific pattern of standing
outside, registering events from an external perspective. Events in data science are
constituted not from experiences but from those traces of experience which can be
datafied. The consequence is the same as it was for science: a displacement of
significance away from direct apprehension. Data science echoes neoplatonism by
moving to a point of view ‘against experience’. Moreover, the scale of operations with
data makes the processes inaccessible to us directly. Thus, data science prioritises data
over the phenomenological and uses this to reveal mathematical orderings.
But does data science actually propose that the revealed order is ontologically
superior? Data science as such does not claim to reveal causal relationships. In fact,
it substitutes correlation at any cost for causal mechanisms and is not constrained by
any wider framework of consistency, unlike the physical sciences. And yet, at the same
time, it is increasingly enrolled as a justification for action in the world. How can a
method which simply reveals patterning become so influential in terms of decision-
making authority? This comes from the continuation through data science of what
critics of science would call ‘onlooker consciousness’. We perceive ourselves to be
standing outside of a reality which we observe and manipulate. This is, in fact, the
Data Science as Machinic Neoplatonism 261
constituting condition for the possibility of scientific experiment. Nature is organised
into a set of concepts which can be represented quantitatively, and the scientist works
with the organisation of these conceptual representations. In our scientific culture, these
are the preconditions of superior truth claims. Data science enables us to stand outside a
mathematised and manipulable context. Data science seems to fulfil the same criteria as
science and, thus, by extension, accrues a similar authority. In effect, the pronounce-
ments of data science are being treated as ontologically superior without actually
having to make that claim.
The neoplatonic character of data science makes it hard to constrain. It creates the
structural conditions not only for specific injustices caused by bad data or false
positives but also the elevation of epistemic injustice, where data science has more
sway than the testimony of the subject, or where a community is unable to contest the
data science because they lack the capacity to express their knowing in the same way
(Fricker 2009). Where data science provides ‘insights’, the testimony or participatory
understanding of individuals or groups without access to this insight becomes devalued,
even where they are the central subjects of inquiry. Inverting the traditional slogan of
the disability movement, data science seeks to know ‘everything about me, without
me’. Data science as an organising idea rekindles a commitment to a new way of
seeing, and this commitment can transcend contradictory or disappointing results in the
short term. The new paradigm redefines ‘the facts on the ground’,because,asboth
Kuhn and Feyerabend pointed out, the very idea of what constitutes facts can change
with a shift in the overall pattern of thought (Kuhn 1996). Traditional safeguards and
civic protections become ineffective, because the ground they stand on is modified by a
new neoplatonism. The force of the change comes in part from this paradigmatic
reframing, but also because this is a worldview that is at the same time its own
enactment. Unlike previous forms of metaphysics, neoplatonic data science attenuates
the world directly because it is also machinic.
6 Machinic Neo-Platonism
Data science is machinic, because it is an apparatus that not only makes possible a
certain way of knowing but also acts directly on the knowledge produced. In that sense,
it is very different to science, which seeks to distance itself from implementation in
order to retain the veil of neutrality.
The apparatus of data science extends beyond the moment of calculation to include
the networked infrastructures that generate the data and the mechanisms that actuate the
algorithmic judgements. The action may simply be the reordering of updates in your
Facebook feed, and this consequences a minor change in your mood (Kramer et al.
2014). But the cumulative effect of predictions that becomes pre-emptions must be the
foreclosure of life chances. As Agamben said about the state of exception, it has the
force of law without being of the law (Agamben 2005). Data science always has a
target, in the same sense that Husserl characterised consciousness as intentional, that is,
always a consciousness of something (Husserl et al. 2001). As a targeting machine, it
raises complex questions about accountability. But this is different to traditional AI
(artificial intelligence) debates about whether machines are capable of moral reasoning.
If the algorithmic part of data science is AI, then it is AI without cognition or
262 McQuillan D.
apprehension. It is simply savant at scale, a narrow and limited form of ‘intelligence’
that only provides intelligence in the military sense, that is, targeting. Where machine
learning makes reasoning inaccessible, and where the computation itself is subject to
errors which cannot be pinned down, accountability for mistakes acquires a core
However, this assemblage still includes human agency at most junctures. Decisions
for drone strikes are not yet taken by autonomous weapons systems, as far as we know,
and even Facebook’s algorithmic filtering may involve a somewhat blurred amount of
human intervention (Chaykowski 2016). With data science, we have moved from
metadata to metaphysics; it is an embedded, even weaponised, philosophy. Where
humans are part of the data science apparatus, what can be said about the effect on
human agency of data science as an organising idea? By providing actionable numbers
with the aura of authority, the algorithmic predictions become forceful at a human level.
The potential exists to sideline ethical concerns or amplify pre-existing biases. Consider
the way the police spokesperson defended the targeting of black suspects by the
Chicago ‘heat list’algorithm (Gorner 2013). It could not be racist, because it was
algorithmic. Most people charged with reacting to a data science prediction are unlikely
to have the benefit of time for reflection. The social worker given a predictive score for
the likelihood of parents committing child abuse cannot retreat into academic critique
('Government Halts Abuse Prediction Study' 2015). The risk is that pervasive data
science at the level of the social will give rise to more of what Hannah Arendt described
as ‘thoughtlessness’(Arendt 2006). Arendt developed this concept through her efforts
to comprehend Eichmann and his actions. She used thoughtlessness to characterise the
ability of functionaries in the bureaucratic machine to participate in a genocidal process.
Of course, we are not concerned here with fascism per se. But thoughtlessness, which is
not a simple lack of awareness, is also a useful way to assess the operation of
algorithmic governance with respect to the people enrolled in its activities. In wrestling
with the legal basis of the trial that she was observing, Arendt argued that the ability to
judge is a necessary condition of justice: that legal judgement is founded on the fact that
the sentence pronounced is one the accused would pass upon herself if she were
prepared to view the matter from the perspective of the community of which she is a
member. If we are unable to understand the judgement of the algorithms, which are
opaque to us, we are in some way released from categories of intent or accountability.
The result is an apparent indifference to the consequences of following a programme of
action mandated by an abstracted authority.
7 Critiques of Science
What are we to do when faced with an essentially aesthetic epistemology that mas-
querades as empirical, asserting superior insight while remaining essentially blind to
the prior concepts that constitute it as a possibility? If the historical roots of modern
science have made us susceptible to neoplatonic data science, we can look to critiques
of science to help us develop an alternative. Of particular relevance are feminist and
post-colonial critiques of technoscience. They confront head on the idea that there is
only one valid form of science, whose superiority is a product of its internal features,
i.e. the scientific method, the use of mathematics to represent natural laws and the
Data Science as Machinic Neoplatonism 263
scientific idea of objectivity. The main target of their ire is the assertion that there is
nothing culturally specific in the representations of nature that are produced, and they
especially focus on those elements in science which can be traced to patriarchal or
colonial influence. Standpoint theory says that the scientific method and its ideas about
objectivity do not immunise science against these influences. It accepts that the
scientific method is good at removing individual bias or problematic experimental
results. But while some sexist, racist or distorted elements in scientific research come
from not following proper scientific method, others stem from inadequacies in the way
those methods and norms are conceptualised. As Sandra Harding makes clear, a central
weakness in scientific thinking is the understanding of objectivity. Prevailing standards
for objectivity are too weak to identify culture-wide assumptions that shape selection of
specific scientific procedures as good ones in the first place (Harding 1998). Standpoint
theory is concerned with the way that assumptions, discursive frameworks and con-
ceptual schemes generated by certain ways of life shape the way dominant groups think
about both the natural world, and about social relations, and the way those assumptions
get hard coded into the way everyone else gets to understand the world. These critiques
are not saying that science just makes things up, but that any particular form of science
is modulated by the social order in which it develops. Objectivity is strengthened by
dispensing with claims to neutrality that hide its social history.
Without strong objectivity, science can indeed come unstuck. Reardon recounts the
downfall of the Human Genome Diversity Project, an attempt to sample and archive the
world’s human genetic diversity whose main protagonists were some of biology’smost
socially progressive scientists. Despite their good intentions, the project was halted by
outrage from an alliance of indigenous advocacy groups and anthropologists (Reardon
2011, p. 322). Lacking any traction on the entanglement of forms of knowing and
forms of governance, the scientists were caught off guard by questions about power,
especially regarding who gets to make authoritative claims about human diversity. One
aspect of Reardon’s analysis which is particularly relevant to current developments with
machine learning is the way ‘categories used to classify human diversity in nature and
those used to order relevant aspects of social practice’in turn ‘loop back’on each other
‘to produce new societal arrangements’(Reardon 2011, p. 328). Regarding the Human
Genome Diversity Project, she concludes that, despite trying to provide a scientific
basis for the famous UNESCO statements which debunked race as a scientific category
(UNESCO 1952), it failed because it excluded too many people from the debate ‘whose
knowledge might have provided important insights into what it meant to interpret and
define human diversity using the tools of scientific (genetic) experts’, in other words,
by lacking the traits of standpoint theory.
By contrast, a domain of scientific practice where Harding’s ideas have been applied
in practice is molecular biology. As one researcher in reproductive neuroendocrinology
said, ‘I realized that…it was no longer sufficient for me to simply engage in feminist
critiques of science. I needed to formulate a concrete feminist model of scientific
inquiry that spoke directly to my experience’(Roy 2004). For her, the concrete
difference was not in the method at the level of the lab bench, as ‘there is not a feminist
way to pipette, centrifuge, or run a statistical test’, but to draw on the work of Harding
and others in her approach to epistemology and methodology. This altered the direction
of her research into the effect of melatonin on reproduction at the level of gonadotropin-
releasing hormone (GnRH) neurons of the brain. Whereas some clinical trials could
264 McQuillan D.
have justified high doses of melatonin as a contraceptive, applying standpoint theory
revealed an underlying gendered bias in the research programme which led the
researcher to focus on other effects of melatonin on the brain and on cellular compo-
nents such as energy production by mitochondria and to build a case that it should not
be used as a contraceptive. Another area of molecular biology which has seen the
application of Harding’s work is the debate about the contested identity of the HeLa cell
line. The HeLa cell type is the original so-called immortal cell line, first isolated in
1951: human cells that do not quickly die off outside of the body but can be cultured in
the lab for medical research. Decades later, research suggested that due to their many
years of growing in culture, these cells had diverged sufficiently to become a new
species of their own: a regression from human to protist cells that in turn have shown
themselves to be ‘aggressive in invading tissue cultures and extending their biogeo-
graphic range’(Strathmann 1991). The fact that the uninformed and unconsenting
donor of the original HeLa cells was Henrietta Lacks, a black woman from Baltimore
with cervical cancer (Haider 2017) was the starting point for a practising molecular
biologist working with these cells to use standpoint theory to highlight the racial and
gendered bias in this new scientific proposal. Tackling the way ‘metaphors of prolif-
eration and miscegenation enter into and intersect with categories of race and gender in
microscopic discourse’, she deconstructed both the historical scientific debate about
race and the assumption that the presence of papillomavirus type 18 DNA in HeLa cells
mean that Henrietta Lacks ‘slept around’(Weasel 2004).
Despite the incremental acknowledgement of standpoint theory within some scien-
tific domains, the intersection of genomics and data science highlights the tendency of
the latter to pull in the other direction. The multiplicity of variables at play in the
algorithmic pattern finding allows other measures to be used as proxies for race, while
the social complexities of data construction (e.g. how the racial group of a swab sample
is assigned in the first place) are glossed over. A study that combined analysis of
scientific papers on genomics and interviews with academic and biotech practitioners
found ‘a striking trend back toward racial realism in the social shaping of genome
technologies’(Chow-White and Green 2013). The authors conclude ‘that these new
forms of knowledge production are producing rational discrimination’, referring to data
analysis that generates the identification and classification of groups based on the
quantification of risk. To understand the broader social implications, they draw on
theworkofGandy(2009) and in particular the notion of cumulative disadvantage that
‘reinforces and reproduces disparities in the quality of life’(Gandy 2009, p. 55, quoted
from Chow-White and Green 2013). The striking contrast between data science and
Harding’s ideas has been highlighted before, especially in terms of the big data’s
distorting positivism and its distancing effect from notions of race, gender and class
(Jurgenson 2014). The claims of the pure data school must be contested ‘to underscore
that there is no Archimedean point of pure data outside conceptual worlds’(Boellstorff
2013). But a wariness of the data science’s‘30,000 ft view’(Boyd and Crawford 2011)
does not provide enough substance to contest its successes. The aim here is that a
trenchant theorising and historicising of data science as neoplatonic can provide the
traction for standpoint theory to tackle it at every level.
Standpoint theory can act as a counter to the neoplatonic vision of data science.
Firstly, standpoint theory can be used to question data science at the level of meta-
physics and not just at the level of consequences. Secondly, it does not promote the idea
Data Science as Machinic Neoplatonism 265
of abandoning empirical methods but of strengthening them by putting them into
dialogue with plural perspectives. A counterculture of data science refuses to throw
the baby out with the bathwater; it does not abandon the idea that empirical and
mathematical methods of data science can generate valid propositions about the world.
But, like standpoint theory, an alternative form of data science must also tackle the
question of objectivity. As we have seen, data science is slippery on this point; while
not claiming to discern objective reality, it operates through forms of mathematical and
computational objectivity. Combined with a dualistic metaphysics, this results in the
production of an apparently neutral and external authority with the tendency to
encourage thoughtlessness at the point where its judgements are applied. This encour-
ages the scientific perspective which Donna Haraway calls the ‘view from nowhere’:
the objective and neutral view which is by its own definition above, outside of,
unlocated and therefore cannot be held to account (Haraway 1988). She calls instead
for situated and embodied knowledges as the grounding for rational knowledge claims.
This would mean that the way out of a machinic metaphysics that eludes accountability
is to find a form of operating that takes embodied responsibility. Moreover, this
embodiment should start at the ‘edges’. Standpoint theory proposes that positions of
social and political disadvantage can actually become sites of analytical advantage,
because they can challenge hegemonic assumptions while owning their own perspec-
tive. So we can search for a way to develop a counterculture of machine learning by
starting from the perspective of those who are disadvantaged by the current construc-
tion of data science. While Harding’s point that ‘abstractness and formality express
distinctive cultural features not the absence of all culture’may seem to condemn
machine learning as a colonialist project, she is also keen to point out that cultural
influence is an inevitable and essential part of developing forms of science. ‘This co-
evolution of sciences and the rest of their social orders turns out not to just limit the
growth of knowledge, as it always does in some way, but also simultaneously to be a
resource for its growth, enabling different cultures, and different historical eras in the
same culture, to detect yet more aspects of nature’sorder.’The task is not to a seek a
fairness that relies on a neutral definition of what is fair, by maximising standardisation,
impersonality or some other quality assumed to contribute to fairness. A counterculture
of data science is a creolisation of machine learning.
8 Counterculture of Data Science
From what has been said so far, it may seem tempting to dispute data science as just
another form of rhetoric. That is, as a form of persuasive argumentation that acts in the
world. Datafication itself is a rhetorical move, because it is saying that the important
aspects of reality are ones that can be expressed as data. The specific algorithms of
machine learning seek to persuade us that a relationship between features can be
determined by a particular algorithm, that the cost function can be constructed from a
particular probability distribution and so on. Perhaps the reconsideration of machine
learning as rhetoric could point the way to its democratic assimilation. Machine
learning as another form of proposition becomes amenable to the discourse of peers.
Seeing data science as a form of rhetoric rather than a way to X-ray reality would allow
its propositions to be returned to their proper place, as basically political statements that
266 McQuillan D.
need to be debated. And yet, data science is not simply social constructivism by
computational means. It is a material process that participates in social production.
Data science is powerful, because it is an apparatus in the sense that Foucault sets
out: a specific set of material and conceptual techniques that coerce by means of
observation, ‘an apparatus in which the techniques that make it possible to see induce
the effects of power, and in which, conversely, the means of coercion make those on
whom they are applied clearly visible’(Foucault 1988). Data science is an apparatus
engaged in the production of subjectivity. While its claims to ontological authority are
unsound, a retreat to purely discursive critique loses the power of performativity and
drops the material aspect of the philosophy. We need a way to work with the materiality
of data science with a different effect. We seek to mobilise the specific constraints and
opportunities in a way that extends participation and agency instead of reinforcing
dualism and hegemony. We can retain a materialist understanding by viewing data
science through Karen Barad’s idea of agential realism (Barad 2007).
Agential realism draws both from Foucault and from the quantum philosophy of
Niels Bohr to articulate the idea of material-discursive phenomena as the objective
referent for any concept of measurement. In other words, the productive nature of
power in co-constituting the subject on which it acts and the non-dual nature of
observer and observed revealed by quantum physics, are recast as mutually reinforcing
descriptions of a holistic social-material philosophy.
Bohr’s analysis of quantum experiments led him to reject basic assumptions of
orthodox science: that the world is made of determinate objects with well-defined
properties independent of specific experimental practices and that measurements of
these properties can be properly assigned to the object as separate from the agencies of
observation. In other words, the stuff that we characterise through experiments cannot
be said to exist in that defined form between, and independent of, us measuring it
(Barad 2007, p. 196). This breaks with classical, representational science. Instead, Bohr
talks about ‘phenomena’as particular instances of wholeness: the inseparable object
In Barad’s account, phenomena are these inseparable physical-conceptual interac-
tions. She introduces the term ‘intra-action’to signify the mutual constitution of objects
and agencies of observation with the phenomena. In Bohr’s understanding, concepts
are determined by the circumstances required for their measurement—they are specific
material arrangements. A specific arrangement introduces a cut between object and
observation that materialises a specific set of properties while excluding others. Like-
wise, Foucault proposes that the objects (subjects) of knowledge do not exist before-
hand but emerge through discursive practices involving apparatuses (Foucault 1988).
Barad assimilates Foucault’s ideas by expanding Bohr’s analysis from physical-
conceptual devices of observation to the notion of the material-discursive apparatus.
Phenomena are produced by the agential intra-actions of material-discursive appara-
tuses, which are not just measuring instruments but boundary-drawing practices.
Phenomena are specific material configurations, not social constructions, but neither
are they independent of human practices. Humans, according to Barad, are part of the
ongoing reconfiguration of the world: ‘humans (like other parts of nature) are of the
world, not in the world, and surely not outside of it looking in. Humans are intra-
actively (re)constituted as part of the world’s becoming. Which is not to say that
humans are mere effect but neither are they/we the sole cause, of the world’s
Data Science as Machinic Neoplatonism 267
becoming’. Human practices are ‘agentive participants’as phenomena are ‘sedimented
out’of this ongoing process. Agential realism is the notion of material-discursive
practices that produce the world through a process of sedimentation, that is, the iterative
layering of phenomena that produces ‘subject’and ‘object’rather than taking them as
Barad draws on Judith Butler’s ideas about performativity to explain the way agency
emerges from the iterative production of reality (Butler 2011). Rather than a determin-
istic causality, we have constraints, within which there is the space for new possibilities.
Moreover, the agency comes through acting rather than being seen as an attribute of
subjects or objects. It is a ‘matter of making iterative changes to particular practices, in
refiguring boundary articulations and exclusions’. Agential realism reinscribes partic-
ipation rather than reinforcing dualism. ‘If our descriptive characterisations do not refer
to properties of abstract objects or observation-independent beings, but rather through
their material instantiation in particular practices contribute to the production of agential
reality, then what is being described by our theories is not nature itself but our
participation with nature’.
Agential realism does not presume specifics about the world prior to the enactment
of material-discursive practices. Considering data science in this way brings two key
benefits: a non-dualism that contrasts starkly with the current neoplatonism and the
possibility for participatory agency.
By dispensing with onlooker consciousness, the non-dual perspective counters the
ethical split that runs through the neoplatonism of both science and data science. This
ethical split has allowed some aspects of the world to be labelled as object, as opposed
to subject, and therefore open to instrumental manipulation without any consideration
of whether intrinsic harm is being caused. Agential realism sees the production of the
real through participation in material-discursive practices that are constrained but not
deterministic. As a productive machinic process, data science is open to a participatory
The paradoxical result of reforming data science as agential realism is to take it more
seriously than it takes itself at the moment. That is, to see it not as a description of a
hidden layer of reality, but to understand it as part of the production of reality. Agential
realism suggests that the world is ‘sedimented out of the process of making the world
intelligible through certain practices and not others’and data science itself is a prime
example of such a material-discursive practice. Understanding data science through
agential realism is both to dispute its objective knowledge claims while recognising it
as an apparatus whose role in ‘sedimenting reality’is open to participatory reworking.
Agential realism is a guidebook for developing a countercultural data science as praxis.
The problem with countercultures of orthodox science, such as standpoint theory, is
that they stay largely at the level of critique. While indigenous forms of knowledge
production cling on wherever marginalised cultures are able to survive, the net impact
of standpoint theory has yet to touch the vast core of modern scientific practice. A
counterculture of data science, however, can be a critique that also becomes its own
practice. In other words, a machinic form of praxis. Praxis is more than simply
reflective practice, because it also contains the idea of the good, that is, an overall goal
of human flourishing. Instead of techne, a way of being concerned with making things
and with what things can make, praxis is political action as a mode of togetherness
(Arendt 1998, p. 175 ff). A participatory counterculture of data science can develop
268 McQuillan D.
praxis by engaging with social justice. As a form of ‘standpoint activism’, the first task
of a renewed data science is to actively involve outside perspectives instead of relying
on data about them. That is, to recast machine learning as a critical pedagogy where
people and communities are involved in both setting the questions and determining the
meaning of what is found. The demand of a new data science, in line with agential
realism, is the refusal of separation between observer and observed.
Data science has been described here as the operation of machinic metaphysics that
travels like a resonant wave through the medium of our scientific culture. Constructing
a sufficient counterculture means countering its claims at the level of concepts and
attempting a deliberate paradigm shift to a more participatory ontology. But a counter-
culture is not only a set of concepts. An effective counterculture is one that can not only
rebut the claims of the dominant culture but also repurpose its artefacts to construct
something novel. When Theodore Roszak invented the term counterculture to describe
the fusion of hippies and the New Left, he was highlighting the way actual social
formations were enacting a vital critique of the technoscientific worldview (Roszak
1969). It may be harder to conceive a counterculture of data science arising under
contemporary conditions, when ‘everything is a target’(Gharavi 2014) and the most
visible challenge to the neo-liberal order is a resurgent right wing. So, it is interesting to
look at a parallel example where an abstract and potentially alienating mathematical-
computational method has been assimilated by a progressive social movement.
The mathematics of graph theory is arguably as abstract as anything in the field
of machine learning. It abstracts the context under study to a set of elements
(nodes) and their connections (edges). In its applied form as social network
analysis, it has, like data science, found eager application in technology platforms
whose business consists of networked relations. As a way of understanding social
behaviour, it can be as alienating as anything that data science can produce, in the
sense that it is also an instantiation of onlooker consciousness with an overt
mission to manipulate (Pentland 2014). And yet, the social struggle against a
right wing and Islamist regime in Turkey has produced a project called Graph
Commons that repurposes network analysis as a collaborative community-led
activity (Graph Commons 2016). Graph Commons aims to ‘empower people
and organisations to transform their data into interactive maps and untangle
complex relations that impact them and their communities’. The initiative gained
initial momentum by creating ‘Networks of Dispossession’, a network mapping of
the complex political-commercial connections behind the destruction of Gezi Park
in Istanbul (Grant 2016). It seems that the idea of mapping networks makes sense
to the participants as a form of critical pedagogy: as a way to help reshape shared
understandings in the context of an active social struggle. The ongoing form of
Graph Commons is conceptual, practical and aesthetic. The core of the project is a
technical platform that makes the computation and visualisation of network
mappings accessible in a way that does not rely on mathematical ability (Arikan
2016). But there is an equal emphasis on outreach to diverse potential user
communities through participatory hackathons (Graph Commons Hackathon
Data Science as Machinic Neoplatonism 269
2016). Without overvalorising this single example, we can count it as an attempt
to develop a praxis using abstract conceptual-computational means.
The example of Graph Commons supports the contention of standpoint theory as
applied to data science. The development of a relevant praxis of countercultural data
science must also reach out to the social edges, however they are defined. As Harding
says, ‘to get a critical perspective on...conceptual frameworks, research must begin
from the ‘outside’.Standpointprojectsdothisbystartingresearchfromthedailylives
of social groups that are not well served by dominant institutions’(Harding 2010).
Bolstered by an agential realism that resonates with the technical means to hand, it is
possible for movements in data science to emulate initiatives like Science for the People
(Science for the People 2013) or science shops (Wachelder 2003) and develop authentic
forms of ‘machine learning for the people’. The metaphysical will meet the machinic at
the point of relevance to social struggles. It is plausible that machine learning can find
this common ground, given its characteristic of making connections. By exploring and
positing new forms of correlation and association, and doing so freely without any
claim to superior knowledge, machine learning could become part of an apparatus that
promotes mutuality and interdependence. Not so much data science, as data solidarity.
270 McQuillan D.
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