dataesr/scientific-paragraphs-categorization huggingface dataset:
Zizi-Queering the Dataset is part of the broader The Zizi Project, an ongoing series of artwork where Elwes deals with the intersection of the rhetoric of Artificial Intelligence systems-and specifically of Deep-Learning technologies-, the sociocultural aesthetic that performs gender identity, and the ideologies that are entwined with these two phenomena. The articulation of these issues is also at the core of these pages and functions as an analytical lens in the understanding of how the visualization of big collections of visual data can produce knowledge, and specifically, how it can account for an epistemological reading of computational data. The main aim of this paper is to utilize the analysis of the aforementioned piece of art as a tool for comprehending the epistemic perspectives underlying the computational visualization of a particular type of digital archive, the large corpora of digital images.
These collections can be thought of as digital archives where data are gathered and shape the database of the collected images necessary for the training dataset of a Machine-Learning system. As in every archive, these large collections of data need to be situated in a "measurable and numerically-controlled space"  (Dondero 2020: 101). Within Zizi-Queering the Dataset the space in question is the latent space, a virtual spatiality where what the Neural Networks used for the creation of the artwork have learned from the training datasets is archived. It is a mathematical spatial representation of synthetic data and its visualization, normally concealed to the human eye, can be useful for understanding how Artificial Intelligence produces meaningful effects.
Queering AI with archives The curatorial statement of the exhibition can be of help and may be interpreted as a first paratextual tool capable of framing the artwork, in its reference to the Marrian-Webster dictionary definition where the preternatural is described as: "that which exists outside of nature and exceeds what is natural or regular. It is the extraordinary, and inexplicable by ordinary means" (2019 w/p). 3 Exactly on the frontier between the ordinary and the extraordinary does Zizi-Queering the Dataset install its artistic proposal: the video makes visible the transparent nature through which Artificial Intelligence builds its rhetoric by rendering it intelligible, redeeming it from the inaccessibility with which the uninitiated normally deal. When mentioning the category of transparency, it is necessary to refer to the work of Louis  Marin on visual enunciation (1994).
For the French theorist, transparency is the representational quality that results from the process of concealing the material and codified character that shapes visual enunciation. Following this perspective, it is also possible to go a step further and consider that no image transparently argues itself in relation to the epistemic boundaries it involves: in visual enunciation, we also assist in the presentation of a representation  (Dondero 2020). In order for the enunciation to take form, an image can manifest itself transitively-by representing something-and reflexively-by presenting a representation that reflects on its composition. In the same way, Zizi-Queering the Dataset reflects on the latent dimension of the "measurable and numerically-controlled space" by making it opaque, and visible.
Thanks to this gesture of presenting a representation the training space where the dataset was fed, and which is normally concealed, is now visualized. This reification permits us to make our first analytical proposal: to consider the continuities of meaning that exist between archives, as collections of sociocultural data, and datasets, as empirical agglomerations of data archiving what an Artificial Intelligence knows. To probe deeper into this proposal, we will recur to a pragmatic understanding of archives within a semiotic perspective founded on a Peircean reading. Since the last decade of the 20 th Century, the production in relation to Archive Studies has increased exponentially, a growth that marks the feeling of an epoch. As reported by Patrizia Violi (2014) the archive can be studied both as an iconosphere, a shared repertoire of a local visual encyclopedia linked to a certain event; and, in the terms of  Michel Foucault (1969) , as a device of sayability, capable of generating new discursive forms and sociocultural practices.
At the same time it transparently archives, the archive also produces knowledge and has performative, and thus opaque and reflexive, consequences concerning what and how something is informed and remembered. 4 ithin this perspective, if we analyze an archive semiotically, by recovering the sign definition provided by Charles S. Peirce (C. P. 2. 218) 5  it is possible to affirm that an archive, as a sign, is something which stands to somebody (a certain criterion that postulates the data to be remembered ) for something (a concrete information forged in an experience or an artifact that operates as a mnemonic substitute) in some respect or capacity (according to a certain classification). By applying this semiotic perspective, the archive, intended as the large corpus of data that can be used within a Machine-Learning system, makes sense and produces knowledge when it organizes that computational criterion that removes the collected database from its amorphous storage, engaging it in a computational machine learning operation.
This is why if we consider archives as the attempt to re-enunciate sociocultural data, we find parallels with data collection efforts in Machine-Learning. From this viewpoint, it is possible to state that archives and Machine-Learning datasets can pursue common goals: Recent fairness initiatives in the Machine-Learning community echo procedures and language already developed and used in archival and library communities (…) [such as] guidelines for how to label data; the collection and accessibility of private information; sharing datasets across platforms; critical reflections on diversity and inclusivity; theory of appraisal and selection (Jo & Gerbu 2020 w/p). Nevertheless, at the same time, archives and Machine-Learning datasets are traversed by differential characteristics due to the phenomenal and epistemic distance that can exist between the curatorial practice within an archive (Cáceres Barbosa and Voto 2021) and the minimally supervised labeling within Machine-Learning.
Queering AI art with epistemology The series of video portraits that compose Zizi-Queering the Dataset is shown through the visual effect of facial morphing 6  as the result of a particular training of a Deep-Learning system. Thanks to this artistic gesture the work of art helps us to understand the main hypothesis underlying these pages: the firm belief that all the diverse technologies that engender visuality have a differential meaning-effect on the epistemic reason of the engendered visual artifacts. Stressing deeper our hypothesis, we can affirm that in those artefacts where gender divide is assumed as a visual factum the transparent quality of the enunciation conceals the material and situated conventions that regulate such representations. These conventions, in a historical and highly codified genre such as portrait, are based upon determined and measurable gender features that have been normed through the perspective of disciplines such as eugenics and anthropometry.
As affirmed by Wendy Chun: "the links between eugenics and recent studies on facial recognition technology are not only topical or aspirational, but also methodological  " (2021: 194). In this regard, the rhetoric transparency that, throughout centuries, has made the gender divide as a biometrical and thus quantitative and verifiable fact (alike the relation of jaws to cheeks, the prominence of Adam's apple, the relation of nose width to eyes, and relation of forehead to hairline, etc. ) also shaped the conventions of biological phenotype scientific representations, as we will see in greater detail in the next section. This prescriptive and normative legacy still reaps its bitter fruits today through the features regulating automated recognition systems and database collections with which Deep Learning systems are fed.
Challenging, or perhaps we should say, repairing this legacy Zizi-Queering the Dataset aims at ungendering the computational facial recognition parameters through the visualization of the latency that made binary gender divide as a visual factum. Through its visual strategy, the work of art makes the computational process on the determined and measurable designed facial features tangible and shareable. By visualizing the latent space, that is, by rendering visually intelligible the filtering, recomposing and compressing of the facial visual data gathered in the database, it makes opaque to the presentation of the gender representation, in the terms of Marin. At this point, it seems necessary to provide a methodological clarification. While Artificial Intelligence, Machine-Learning, Deep-Learning or the Generative Adversarial Network may seem to be the same thing, they are not, although there is a deep connection among them.
Restricting ourselves to a computational vision we can say that nowadays, as far as the design of Artificial Intelligences that are capable of seeing and interpreting images is concerned, this defeat has been inflicted thanks to the hard blows dealt by the archive policies of data collections used as databases to feed technologies. Building an Artificial Intelligence system always requires datasets that, in the words of Kate Crawford and Trevor Paglen: "Shape the epistemic boundaries governing how AI systems operate, and thus are an essential part of understanding socially significant questions about AI" (2019 w/p). This frontier is the epistemological discrimination between what will be archived and what will not, what will be labeled and what will not, determined by the selective criteria of classification that 7 However, the design and theoretical proposal for a machine that could replicate some traits of the human mind began to be advanced as early as 1949 with the text Cybernetics: Or Control and Communication in the Animal and the Machine by Norbert Wiener.
According to the author, it was possible to find similarities between biological and artificial systems through the idea of feedback: a common phenomenon liable to establish a predictability subsumed by progressive mechanisms of hypothesis checking. In a true cultural zeitgeist, one year later, in 1950, a young Alan Turing wrote the essay I. -Computing Machinery and Intelligence hypothesizing the possibility of intelligent machines. organize the archive. Furthermore, the epistemic stakes rise dramatically, as do the ethical repercussions in the design of human-computer interaction, when Artificial Intelligence is not programmed with logical correlations but is allowed to program by learning on its own as in Machine-Learning and Deep-Learning. Within these technologies, the artificial learning experience is trained on datasets that function as the empirical archived knowledge to which the system has access.
Training consists, in fact, of parameterizing a function for input signals to produce the expected information at the output and, in the case of unexpected results, it will be the technology that has to adjust its outputs autonomously. Datasets are therefore fundamental for acquiring the competence and performance necessary for Machine-Learning since "the system cannot acquire knowledge beyond the data that it is fed, unless it comes a priori, encoded in the data itself or in the system" (Audry 2021 w/p), as we saw concerning the binary divide as a visual factum. Providing a description of the operation of Machine-Learning related to the epistemic component that its performance can nourish, we can say that when faced with a given task, it is the system that adjusts its model to improve performance on the dataset.
The dataset, the model, and the training process are the components that allow the system to use the data through decisions by means of an evaluation function that measures skills and behaviours to permit the performance. In the case of Deep Learning, instead, considering it a sub-form of Machine Learning, in order to represent and interpret patterns present in the archived large collection of data, several interconnected layers of artificial neurons are used. In this regard: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. (…) Deep learning discovers intricate structures in large datasets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer  (LeCun et al.
Giffney (2004)  defines queer as a "site of permanent becoming. " Zizi makes this aspect of drag explicit, through autonomously generated faces that are fluid and never still. Here, the permanent becoming of a Generative Adversarial Network represents the fluidity, ambiguity and transition of drag artists" (Hemment 2019 w/p). Semiotically speaking, the latent space is made up of differentially modalized data and, in this perspective, of different interrelated spatialities with which to distinguish the place of the enunciation from the enunciated place. It is the virtual space that maps the actual representation of data learned by a Generative Neural Network, making it real through the potential of the Deep-Learning system. To visualize the latent space in Zizi-Queering the Dataset, Elwes started by using a specific large collection of images, a dataset containing 70,000 photographic portraits of faces-precisely the Flickr-Faces-HQ Dataset 9  -and re-trained it with the addition of 1000 images of drag and non-binary portraits "scraped from various websites and social media profiles"  (Hemment et al.
Queering AI art with portraits Within an artistic perspective, Zizi-Queering the Dataset can be inscribed in a visual genre that has crossed Western Visual Culture for centuries, namely, the portrait and, in this regard, it presents its major characteristics. The video shows a series of portraits where: i. the relationship between the figure and the background is specifically designed, so that the background appears rather neutral to allow the figure to emerge; ii. the figure is positioned in the center of the frame and appears particularly compact in relation to the background; iii. the figure is portrayed through a plastic pose  (Dondero 2020). But the work of art also reflects another characteristic that constituted the genre, that is, the consideration of the aesthetics and ideologies that cross visual culture.
The video installation, in fact, makes manifest the lack of visibility of non-binary facial aesthetics within databases, prompting a profound dialogue between artificial and human intelligence and their visual rhetoric. The visual culture of each historical epoch, with its aesthetics and biopolitical manipulation that allowed the visibility of certain faces and not others, has determined the rhetoric and pragmatic purpose of the visualization of the face, enabling also a reading performed on the base of a resemblance or tolerated on the grounds of an idealization  (Leone 2019 ). In addition, the material dimension that has supported all the facial aesthetics and ideologies that cross visual culture must be taken into consideration. There has always been, in fact, meaning inherent to the design features that have regulated faciality and facial artifacts, properties configured internally, or attributed by individual uses and collective habits.
In this regard, it is possible to affirm that video art inaugurates an ode to a facial present, a face that exists in the present tense, in the time of its own unfolding. An example of this tensive drive can already be found in the work of Bruce Nauman, at the dawn of the first experiments in video portraiture  (Belting 2017). Nauman makes a series of videos entitled Art Make  Up (1967)  where we witness the progressive separation of the facial type from the face token and the metamorphosis of the face into a living mask, just as in Making Faces  (1970) , where it is possible to watch his face becoming, in a present tense, a mask. In this same perspective, Zizi-Queering the Dataset also presents a live making of a face but through a tensive, synthetic, and algorithmic morphogenesis. The hairstyles, cheekbones, noses-that is, all the facial phenomena that make up that paradigmatic unit we call face-are all generated by Artificial Intelligence and reproduced by the visual strategy of morphing.
Queering AI art with visualization In Zizi-Queering the Dataset, the visualization of the latent space through video portraits works as a diagram in the Peircean way. For Peirce, diagrams depend on conventions that, thanks to their iconic aspect, take form in relational patterns that do not refer phenomenally to the object they represent but to the relationality with which cognitive processes must interface  (Paolucci 2010). We saw how, in the artistic gesture of visualizing the latency of Deep-Learning by the choice of the portrait genre, the epistemological boundaries of facial big data intersect with the visual rhetoric that concerns gender/genre aporia. The intersection between the epistemic boundaries and the visual enunciation determines the acceptability and the representativeness of specific portraits in visual culture.
In this regard, since the rhetorical function always has the effect of reifying discourses to move them away from utilitarian use  (Group μ, 1982) , visualizing the latent space also means rendering intelligible "the latent uncertainties in big data archives  [and]  encompass  [ing]  the unknowns or unknowables that we are blind to"  (Thylstrup et al. 2021: 316). Visualizing both facial big data and gender/genre rhetoric permits a diagram to emerge that drifts from a specific meaning production, previously built from the sexual binary division that ascribes specific pertinence and values to identity. The diagram that Zizi-Queering the Dataset allows us to visualize interfaces us with the gender identity-making configurations of meaning enacted through artifacts, practices, technologies (de Lauretis 1987), and data.
This diagnosis of the present time of our visual culture, however, can be mitigated: a return to the epistemologies of big data visualization and works such as Zizi-Queering the Dataset certainly represent the cure we need. Throughout these pages, the proposal to retrieve the archive category for dataset analysis has served as a starting point for the comparison between human and artificial intelligence regarding the knowledge gathered in a collection of data. Successively, we considered the different artificial intelligence systems we are confronted with in our digital iconosphere to frame the epistemic problems arising from the increasingly widespread presence of big visual data and, particularly, facial big data. Today, when the aesthetics of portraiture have to deal with automated and computational affordances for the interfacing of socio-cultural identity, reflecting on the epistemic dimension of big data by recovering the concept of the diagram as understood by Charles S.
It is in this age that the exhortation to "exploring and analyzing what is missing from a dataset (…) and interrogating a dataset's validity-that is, the degree to which it can be said to represent the concept being analysed-" (D'Ignazio & Klein 2020: 160) is absolutely necessary. For this reason, we chose to analyze a work of art such as Zizi-Queering the Dataset because the reflexivity guaranteed by the gesture of visualization of the latent space is capable of making us contemplate more egalitarian future uses of facial big data toward increasing fairness awareness concerning the collection of big corpora of data. The traces left by the information designer Data visualization and enunciation punctum. gr KEYWORDS:.
In their work, data journalists must first find a database on which to base their research; second, they must analyze the data bearing a number of elements in mind: the sources must be reliable, the possible correlations plausible, the numbers normalized. Thirdly, they must build a narrative and make the data tangible in stories that clarify their meaning. 12  In the same way scientists, after examining an object of analysis and extrapolating the elements that are most significant for their research aims, must translate them into a technical language if their goal is to keep the message within their professional area, or into a language that can be understood by a wider audience if their purpose is popularization. Designers supplement these skills with their greater familiarity and wider and more refined stylistic repertoire for the visual elaboration of the message.
From our perspective, situated in the methodological framework of visual semiotics, and of the semiotics of scientific discourse, it might be interesting to pay attention to the whole process of constructing knowledge (and visual information) from data, understood as a chain of "devices of visualization"  (Bastide 1985a (Bastide , 1990 (Bastide [1985b]] , 2001), investigating data as a channelled result, and also visualization strategies of specific-and oriented-discourses across data. In one of his more programmatically oriented essays, What is visualization? (2010), Manovich points out that media visualizations based on big cultural data, the huge media databases under his analysis, could more simply be called direct visualization. This is because visualizations of such large datasets would operate without mediation, presenting all visual data simultaneously and synoptically in a single visualization, a kind of image of images.
In this perspective, the set of original images is virtualized when a pattern between and across images emerges. If, then, media visualization, due to its complex nature as an "image of images," is a set of virtualities, from the point of view of an observer taking it as a starting point for subsequent investigation and interpretation paths, does it suffice to interpret it without presupposing any discursive mediation that structures it as a signifying object? In other words, should such large datasets of images (such cultural Big Data) be considered only as a pure display of "a corpus as artifacts," with the sole aim of representing "the structure by showing all the elements which constitute it"?  (Dondero 2020: 103). Would such collections of images merely constitute a virtual system (and a non-text) from which to actualize specific visual texts? In fact, the adoption of this point of view seems to overshadow that even the media objects of media visualization, before any further elaboration, can be considered a discursive construction.