
problematic applications, this article instead argues that, to avoid becoming a vector of data science's politics of completion, legal narratives should highlight the fundamental contingency of computational empiricism and its knowledge claims.

This article begins with a historical and technological contextualisation of personality computation. It looks at similar exercises from the past, and tracks the history of combining photographic portraiture with statistical analyses. That section demonstrates the ongoing ideologies behind this type of profiling, and how they are serviced by 'big data epistemology'.

However, this is identified as only one of the political problems of this type of classificationanother being the reverence for the forms of empiricism operationalised. Through the historical analysis, the idea is to demonstrate not how statistics and mathematics are becoming instruments of real or symbolic violence, but rather the nature of the change from visual to statistical forms of knowing. The article then seeks to sketch an outline of this emerging computational empiricism by connecting the knowledge claims of computer vision profiling to antecedent media technologies. The goal is to understand the ongoing role of 'representation' within the systems of measurement at work in computer vision, and the politics of their dissimulation. Finally, the article explores possibilities for legal and technical intervention. In that section, it is argued that the narratives animating existing legal protections are becoming deficient in the face of technological practices that see humans in a new way, primarily as patterns of information. Accordingly, the article suggests that when dealing with profiling, privacy and data protection now need to focus as much on 'metaphysics' as they do on 'metadata'. 21 Rather than the elimination of bias or unfairness, or the total prohibition of certain applications (both of which have their place within legal armatures), the argument here is for legal mechanisms that marginalize purely computational ways of knowing. The goal is not to 'put the genie back in the bottle' but rather highlight and contest the ontological power of computer vision when classifying people. physiognomy had already been scientifically discredited by the time of Galton's experiments in 1877. However, belief in photography's 'mechanical objectivity' gave Galton a new (though crude) statistical tool to investigate his hypothesis of biological degeneration. 26 Galton's experiments were ultimately failures. He found that the visual similarities of the criminal classes disappeared through the composite process which instead revealed the 'common humanity in all'. 27 Despite this failure however, and despite the stigma associated with eugenics and 'Social Darwinism', the experiment has been repeated using new photographic techniques, statistical methodologies, and biological theories. One important differentiation however, is the movement in knowledge paradigm from qualitative visual searches for similarity amongst groups towards purely quantitative statistical analysis.

In parallel with ongoing physiognomic experimentation, there has been a growing psychological literature exploring how 'first impressions' are generated from looking at faces (including impressions of criminality). 28 That material demonstrates how faces are a form of non-verbal communication and part of the intrinsic heuristics we use to navigate daily social interactions. However, it has also led to the claim that 'research on appearance-based [personality assessment] might seem more credible if society were "not so enamoured of the idea that because a person's appearance ought not to make a difference, it does not," blaming the dearth of this research on the "naturalistic fallacy" (that is, confusing how things are with how they ought to be).' 29 In rejecting the 'naturalistic fallacy', the theoretical justifications for this work have at least moved away from the biological or genetic determinisms of Social 26 Lorraine Daston and Peter Galison explain the term 'mechanical objectivity' in their text Objectivity (MIT Press, 2007), 121 as 'the insistent drive to repress the wilful intervention of the artist-author, and to put in its stead a set of procedures that would, as it were, move nature to the page through a strict protocol, if not automatically'. They note how the camera could 'quiet the observer so nature could be heard', and There is something historically significant in physiognomy being a platform for the advancement of computational empiricism. Criminologist Nicole Rafter has brilliantly argued with respect to the earlier (analogue) practices of physiognomy and phrenology that their scientific invalidity was insignificant when compared to the epistemological paradigm that they ushered in. 50 Those pseudo-sciences shifted how we understand people and their behaviour away from metaphysics and theology and towards 'analytical empiricism'. Rafter shows how phrenology, the discredited science of 'the correspondences between the external and internal man, the visible superficies and the invisible contents' 51 but based on reading character traits from skull morphology, therefore 'produced one of the most radical 46 Wang and Kosinski, above n 12. 47 The results were post-hoc justified with a prenatal hormone theory (that over or under exposure of androgens during gestation affects both facial appearance and sexual orientation), which gave a biological foundation to the statistical findings. 48 
Computational Empiricism
The claim that nature can only be understood through computation articulates data science as an attempt to access the hidden mathematical sub-structures of reality. In many respects, this claim is neither unique nor controversial, but it remains significant in the context of a technological capacity that is proliferating so rapidly and widely. It is also far from universally accepted. Some computer scientists sensibly argue that 'deep learning can't extract information that isn't there, and we should be suspicious of claims that it can reliably extract hidden meaning from images that eludes human judges.' 57 But such admissions seem peculiar when, really, deriving meaning from measurements too granular for non-computational analysis is the very premise of machine learning. Further, as argued, the accuracy of those applications is unlikely to be the primary determinant of whether their outputs are interpreted as "true". To that end, some have begun to argue that claiming access to the hidden structures of reality has now become the organizing principle of data science.
58
Many technologies of representation claim to reveal previously unseen information. 
Computational Empiricism as a Dominant Epistemology
As society becomes more statistical, 86 it makes sense that both representation and knowledge take less deterministic forms. However, from the above, we can begin to draw an outline of new forms of computational empiricism (or positivism) as applied to understanding people. 87 First, it operates on the basis that external measurement or observation is a more reliable pathway to knowledge than the symbolic output of a subject. This is, of course, not unique to these practices or technologies, it is simply one element of the schema. It is also not a uniquely visual phenomenon. In addition to the techniques described above, the His point is to suggest notions of 'logical empiricism' require updating by computationally oriented methods. The term 'computational positivism' is best described in Narasimha, above n 34, where he uses the term to describe methodologies in exact sciences and mathematics that focus on matching algorithms to observations rather than drawing conclusions from axioms and models. 88 See Jonathan Sterne, 'Mediate Auscultation, the Stethoscope, and the "Autopsy of the Living": Medicine's Acoustic Culture' (2001) those older techniques, one that is simultaneously interpreted and constructed an informational pattern rather than embodied puzzle.
Computational Empiricism as a Dominant Epistemology
A second element to the schematic is a specific type of computational intervention in the relationship between measurement and classification. On one hand, computational systems differ from other representational technologies because of their capacity to both measure and process quantities of data that are too large for human tabulation, too discrete for human perception, and too complex for human cognitive analysis. This is also not unique to computer vision or data science. Where these techniques differ however, is the degree of automation A third element is a belief that this process is working towards exposing the fundamental substructures of reality. The belief that increasingly granular measurement and high dimensionality analysis has the capacity to reveal hidden truths is visible to a greater or lesser degree in research projects using high resolution sensors and learning applications. This computational variant of realism has been described by Dan McQuillan as 'machinic neoplatansim'. 91 That is, a metaphysical commitment to a world of truth, form, and idea existing behind, and only imperfectly imprinting on, the world of the humanly sensible, accessible only through mathematics. Rather than being a specific tool or method, for
Computational Empiricism as a Dominant Epistemology
McQuillan, data science thus represents an automated and applied philosophy, maintaining an epistemological reverence for '[a] hidden layer of reality which is ontologically superior, 91 McQuillan, above n 21, 261.
Computational Empiricism as a Dominant Epistemology
expressed mathematically and apprehended by going against direct experience.' 92 None of these observations are unique to technologies of computer vision, the practices of machine learning, or the disciplines of data science. However, when schematised and directed at understanding people, they represent an idiosyncratic system of knowledge production that challenges the way legal narratives (as well as many others) have been deployed to protect individuals in the context of online profiling.
Law in the World State
A classic critique of technology insists that technological mediation inhibits access to 'the real' or 'the event'. In the case of photography for instance, we are reminded that the images we create, while supposed to be windows or maps for understanding the world around us, actually operate more like 'screens'. Rather than expose the truth of the world, our images saturate the world, producing a veneer under which 'the real' slowly decays. A form of this critique is often levelled at digital profiling, wherein data produced through interactions with information environments are used as proxies for defining characteristics about us. That is the world of actuarial risk assessment and the 'scored society'. Critiques of those technologies describe how such 'scores' inadequately capture or represent individuals. Proxies result in reduction, distortion and error. 93 Justin Clemens and Adam Nash offer a useful account of this process wherein information 'must first be digitised to data, then modulated between storage and display in an endless protocol-based negotiation that both severs any link to the data's semantic source and creates an ever-growing excess of data weirdly related to, but ontologically distinct from, its originating data source.' 94 In other words, the mathematical codes and conventions used to analyse and parse already hyper-mediated digital information omit or marginalise their 'natural' starting point. Clemens and Nash thus claim that only through modulation into a display register does digital information obtain meaning. 95 This intervention frames the harm to persons from profiling in terms of loss. It also grounds, for Legal thinking has so far responded to these technologies and techniques in different ways.
Law in the World State
One register seeks improvement of automated systems' encoding of the world. The goal is to achieve a 'fairer' computational translation of the real world by exposing and limiting bias and prejudice. Unfair discrimination can find its way into automated systems in multiple 103 This is typically a more traditional 'privacy' mechanism defending the fundamental dignity and opacity of persons against overreaching applications. These projects similarly deploy their force at the level of application rather than at the epistemological or political bases of computational empiricism.
Law in the World State
Beyond abandoning or optimising the technologies however, there remains an important space for demonstrating the contingency of computational empiricism's knowledge claims and challenging data science's move from 'metadata' to 'metaphysics'. 104 This is what Mireille
Law in the World State
Hildebrandt, for instance, means when she talks of 'speaking law to the power of statistics'
105
-a program of re-inscribing uncertainty into automated knowledge production.
Law and Computer Vision Profiling
There are already important legal limitations on automated decision making and automated profiling in, for instance, the GDPR. Alongside well explored 'access and rectification rights', 106 and the fundamental principles of processing in Article 5, 107 Article 22 gives data subjects rights to not be subject to automated profiling decisions based solely on automated processing where those decisions produce legal or similar effects. There are numerous limitations to that latter provision, including explicit consent, enabling legislation, or satisfaction of a contract. 108 What constitutes legal or similar effects, as well as purely automated processing, are also unclear. That a human decision maker anywhere in the process might remove an automated system from the purview of the Article seems a problematic limitation, especially considering the growing evidence that human decision makers rarely contest the outcomes of decision support systems. Further, guidance by the these provisions. A right to 'explanation' has also been read into Art 15(1)(h), producing
Law and Computer Vision Profiling
rigorous debate over what the Article truly affords data subjects, and how useful it may be.
109
While a similar (and arguably broader) provision has been in place since 1995, a broad reading may give data subjects useful mechanisms for not only understanding, but properly contesting and challenging automated decisions. However, it still places a substantial impetus on the data subject to protect its own rights, and only indirectly challenges the knowledge logic of computational empiricism. Explainability may even be harmful if entrenching automated decision making and narrowing the types of reasons to be given for decisions and thus the grounds for contestation. Being subjected to automated decisions without understanding how or why that decision was made may be problematic. But receiving inadequate automated explanations without recourse might be worse. In that format, 'explanation' similarly becomes a legal vector for proliferating decision automation. However, other approaches appear to more directly challenge data science's epistemological dominance. measurements, representations, and analytics. Rather than challenging human computability wholesale, the project highlights how humans can be computed in multiple ways, in order to 'ward off monopolistic claims about the 'true' or the 'real' representation of human beings'. 112 This is arguably also a form of explanation and accountability, but one targeted more appropriately at the decision making process itself rather than the relationship between input and outcome (i.e. counter-factual analysis). In other words, an explanation mechanism informing how to transcend the solution space of any particular decision. This type of mechanism is appealing because it more directly addresses the construction of the human subject as information pattern. It also recognises the significance of representations in a manner similar to technical approaches like 'disentangling the factors of variation' in order to produce 'disentangled representations'. 113 What this manner of legal intervention would actually look like requires more legal, conceptual, and technical thinking however. But it at least offers a pathway to envisioning new projects built for a technical age that sees humans in a very different way than our existing legal systems insist on seeing them.
Conclusion
This article has attempted to outline some emerging challenges produced by a profound new technical capacity. Lawmakers now have to contend with a radically extrapolated enlightenment philosophy insisting that measuring everything and drawing knowledge from those measurements is the path to truth. Permitting such measurement and classification without limitation risks generating a fair, transparent, and non-prejudicial totalitarianism.
Conclusion
This has been described as the 'insanity proper to logic', whereby measuring everything, logic conceives 'a world in which all things are relative, makes itself absolute, and denying the whole of nature, establishes its own artifices.' 114 Addressing this reality means clarifying how computer vision systems and data science applications are merely apparatuses, combining and computing symbols that encode the world a particular way, according to particular instead of producing and operationalise a para-reality built from para-visual representations.
Conclusion
Computer vision has been presented as one vector of that knowledge logic, and one target of a new kind of legal thinking. However, this is certainly not the case for computer vision alone.
Conclusion
It is relevant for any data science application translating the real world into the symbolic The Hedgehog Review (online) < https://iasc-culture.org/THR/THR_article_2018_Spring_Weatherby.php> 116 Flusser, above n 19, 10.

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