This text acts as something of a primer and homage to some of the work of artists, curators, writers and thinkers in this field, who have been exploring data-related art and the wider cultural impact of data. Over the past two decades, many artists, curators and exhibitions have addressed the transformation towards increasingly digital and ‘datafied’ societies.[1] It is both through research into these histories of data-related exhibitions, as well as into the work of each of the exhibiting artists that Data Relations has taken form. Art made with and about data holds up a mirror to the changing social, cultural and political landscape of our times, both off and online.
In the first decade of the turn of the twenty-first century, a wide array of exhibitions called attention to artists working with the aesthetics of the database, enhanced access to live data sets[2], and the wider impacts of greater general engagement with the internet.[3] Into the 2010s, the seismic developments brought about by the ‘discovery’ of ‘big data’ prompted a wide array of responses to the economic and political dimensions of data, from government surveillance leaks to exposés of the bias baked into data sets used in machine learning.[4]
Artists have increasingly grappled with this field over the past decade, homing in on the nature of being human in a data-driven age, and occasionally identifying alternative opportunities to use data for ‘social good’.[5] Similarly, there emerged a strong drive to contextualise and understand the origins of data, achieved with varying survey exhibitions spanning digital and pre-digital works of art and other related material.[6] As we hurtle into the third decade of ‘datafication’, albeit largely blinded by the spoils of new Artificial Intelligence (AI), our hopes and fears co-exist with a continually widening data literacy.[7]
What is ‘data’?
The most common question asked when discussing this exhibition has been along the lines of ‘what exactly do you mean by data?’, which is also closely followed by something equating, ‘…but what does that have to do with art?’ American academics Catherine D’Ignazio and Lauren F. Klein offer a basic answer for the first question in their publication Data Feminism: ‘Many people think of data as numbers alone, but data can also consist of words or stories, colours or sounds’.[8] Data can be any content, they note, that is ‘systematically collected, organised and analysed.’[9]
‘Data’ of course also now carries the real and conceptual baggage of acting as a slippery shorthand for ‘big data’. Simply put, big data refers to the increased volume of digital data production thanks to the combination of enhanced global access to, and use of personal devices and the internet, and the technological capacity to analyse and interpret data at speeds that are beyond the scope of human ability.[10] However, the extractive logic of late capitalism, particularly as promoted by the Silicon Valley technology giants, also means that ‘data’ is associated with the commercialisation of the collection, storage, analysis, sale and manipulation of big data.[11]
Even with myriad definitions and associations, data is abstract and intangible. Accordingly, experimental approaches to ‘visualising’ data, and communicating what data collection and analysis can look like, have a long history.[12] D’Ignazio and Klein point to the legacy of eighteenth century North American statistician and designer Edward Tufte, whose minimalist approach apparently remains influential in the majority of data visualisation in the form of graphs and charts encountered today.[13] However, data visualisation and art engaged with data, are not entirely synonymous. Not all data-related art falls under the umbrella of ‘data visualisation’.[14]
In the context of contemporary art, conceptual approaches to data visualisation emerge in the late twentieth century. For example, the statistical works developed by German-born, New York-based artist Hans Haacke, have been well-cited as some of the most high profile pre-digital visual experiments that also capture the wider social contexts of data, and its use in art as a political tool. Haacke’s visitor statistics and experience surveys at the Museum of Modern Art in New York were included in the 1970 group exhibition Information, to great controversial effect, and have also subsequently included in an array of data-related survey exhibitions.[15]
New media academic and curator Christiane Paul makes one of the earliest mainstream publication references to contemporary artists working with digital data visualisation in 2003 in the first edition of the Thames and Hudson handbook Digital Art. Writing in the context of early internet art practice, Paul introduces the works of artists using data sets to convey the intangible nature of data’s otherwise inherently ‘virtual’ form.[16] Similarly, in a self-published paper from 2002, new media academic Lev Manovich suggests that art engaged with the visualisation of increasing amounts of digital data offer an opportunity to experience a form of ‘anti-sublime’.[17] He implies that artists who thematically exploit the subjective experience of ‘living in a data society’, could bring the scale of data more within the realm of the human senses.[18] He describes this at the time as the possibility that works of art both made with and about data could convey a form of ‘data subjectivity’.[19] Manovich here in fact foreshadowed many such artistic responses to big data over the two decades that followed, and that continue to underpin contemporary data-related works of art today.[20]
The growth of engagement with data in the context of artistic and wider creative practice has been exponential over the past two decades. Yet, there is scope for many more written curatorial, artist-led and academic discussions of works of art and exhibitions on the topic. In particular, there is growing need to balance both the sense of urgency and sheer enormity of the wider context of data, as well as the diversity of manifestations and impacts of data itself within contemporary art. That is, reflecting on data both a material as well as conceptual concern.[21] Many of the most explorative works on this topic are being written by artists, including the artists exhibiting in Data Relations.[22] Early well known contributions to this field notably include the work of academic and artist Hito Steyerl, who has both asked what big data looks like and what that might mean within the history of the image.[23] Among many other recent critical contributions, is also the taxonomy of data in art that has been drafted by academic and artist Julie Freeman, in an attempt to provide a more accurate description of data as an art material, beyond just ‘data’, for the benefit of future curators, artists, conservators and art historians.[24]
What ‘data’ is not
Asking what data is can perhaps be helped by outlining a polemical stance on what data is not. For example, as artist Zach Blas asserts, data is not information.[25] Although there is a common confusion between the terms. As it is understood in the field of data science, the two are not synonymous but rather the analysis of data produces information. As Australian academic and data practitioner Mitchell Whitelaw explains further, ‘data is the raw material of information, its substrate; information is the meaning derived from data in a particular context.’[26]
Whitelaw makes this point to also emphasise the role that this difference plays in data-related art. He notes that some contemporary artists who use data sets, or who seek to visualises data in some form, will manipulate the ‘data-information’ relationship by subverting or resisting the expected revelation of information for conceptual and aesthetic effect.[27] The issue posed by the confusion and interchangeable use of the terms ‘information’ and ‘data’, Whitelaw notes, is a loss in nuance when engaging with data-related works of art.[28]
Data was, in the heady early days of ‘big data’, likened to oil for its exponential commercial value in tracking, predicting and controlling human behaviour.[29] While data is not a natural resource, some sense of the comparisons is made in the language of ‘data mining’, and the colonial practices of extraction and labour abuses, along with the environmental impacts of vast data storage centres and undersea communication cables, as well as other power relations associated with the data economy.[30] In this vein, English artist and writer James Bridle also argues that the current approach to big data and the feedback loop of accelerated technological advancement, sees the potential risks of data more akin to a nuclear arms race than oil.[31]
Further, as all the artists in Data Relations touch on, data is not something that simply exists a priori. Mimi Ọnụọha notes, ‘data is not an unlimited resource.’[32] Zach Blas highlights that it is also not a source of either magical or ultimate and unarguable truth.[33] Winnie Soon emphasises the malleability of data.[34] Tega Brain and Sam Lavigne argue, ‘data is an abstraction’.[35] Lauren Lee McCarthy stresses that data is ‘always incomplete’. That data is situated. [36] Machine Listening remind us, ‘data isn’t mined, it’s made’.[37] Returning to the initial description provided by D’Ignazio and Klein, the verbs collect, organise and analyse infer the human actions, and inherent world views, that are required to bring data into being. The worldviews and bias of the people involved in determining the parameters and uses of data sets are the groundwork onto which data’s contemporary status of oracle has been constructed. As such, bias is frequently baked in and extenuates discrimination, oppression, exclusion and division.[38] The title of the exhibition,Data Relations, seeks to emphasise this as a starting point for engaging with the works presented by the six artists and collectives, in which data is relational.
Data Relations
The title of the exhibition is alsodrawn from one of the many recent critical analyses of data in contemporary society.[39] In their 2019 publication, The Costs of Connection, academics Ulises Ali Mejias and Nick Couldry, specifically use the phrase ‘data relations’ as one of the key terms to describe the conditions of ‘data colonialism’.[40] Mejias and Couldry explain that, ‘we do not mean relations between data, but the new types of human relations that data as a potential commodity enables.’[41] Noting further, that data relations ‘are the emerging social form through which data colonialism as an extractive process gets stabilized between individuals, groups, and corporations, and so it comes reliably to contribute to capitalism’s emerging new social order.’[42]
All of the artists and projects presented in Data Relations in some way acknowledge, reflect or unpack elements of Meijas and Couldry’s ‘emerging social forms’.[43] Building on the data-information tension and data as inherently relational, the exhibiting artists draw on real-world issues with the data economy yet resist proposing straight forward answers. They each variously work with, decontextualise and manipulate the tools, materials and techniques of the data economy with humour, care and wit, blurring digital and physical, fact and fiction, proposition and realisation.
What might it mean to recreate the internet? Brooklyn-based artist Mimi Ọnụọha asks this question with her short film These networks in our skin 2021, and answers with a speculative proposition for an alternative future built on care and respect.[44] Women carefully rewire cables that symbolise the vast global network carries our data, by interweaving hair, herbs and singing the materials into a better purpose. Here data is connected with people not money and power in mind.
I first heard Ọnụọha speak about what data meant to her artistic and research practice, and why data relations are key to this understanding, in a podcast produced by New York-based research institute Data & Society.[45] Ọnụọha explained that she ‘define[s] data as the things that a group cares about and can measure’.[46] Ọnụọha has expanded on why she uses this definition as background for the presentation of her work in Data Relations:
…it is a reminder that data doesn’t just exist out in the world as some unlimited resource, data has to be constructed, it has to be collected. It is also a reminder of how data is relational. Often you will have a group who collects data, and you will have a group whose information makes up what has been collected. That is the relationship that data emerges out of, and it is one that is marked by shifting power dynamics.[47]
The emphasis on care in Ọnụọha’s practice is striking. Care underpins the artist’s approach to data in her work across sculpture, digital animation, film, installation, as well as in conceptual projects, public speaking and writing. In relation to the latter, Ọnụọha is also well recognised for her recent contribution to data scholarship around identifying terms that reflect on the power imbalances an inequity that datafication has exacerbated. Contesting the assumed neutrality of data-driven, automated decision-making, Ọnụọha has described the converse amplification of structural inequalities around race, wealth, and gender, with the now widely circulated term ‘algorithmic violence’.[48]
Zach Blas’s Metric mysticism: A troll’s tale 2022 goes to the source of much of this violence, tackling the corporate technology sector’s cultivation of data as prophetic authority. Blas is a Los Angeles and Toronto-based artist with a richly research driven practice, which he realises in immersive installations, sculpture, video, participatory projects, and performance lectures. For Data Relations, Blas presents a new site-specific video installation, which expands on the original performance lecture, Metric mysticism 2017–18.[49]
Metric mysticism: A troll’s tale forms part of a larger body of work addressing the cultural practices seeded in Silicon Valley, from the normalisation of hallucinogenic micro-dosing, to data mining. Blas has explained that ‘metric mysticism’ is a phrase he coined early on in researching data analytics companies such as Palantir, which is named after the wizard’s crystal ball in J.R.R. Tolkien’s The Lord of the Rings. As Blas explains, ‘Silicon Valley companies deploy magic, mysticism, and fantasy to conceptualise working with data.’[50] Companies like Palantir have radically influenced public life by pronouncing data as a source of undeniable truth but one that can only be reliably harnessed by data analysts. This is particularly so in the USA, where their commercial correlation products that are based on historical data, allegedly predict crime and automate the justice system, among other critical social services.[51]
… the translation or mediation of data into information has political, social and cultural dimensions to it … in our age of big data, and surveillance capitalism, information is often presented as an unwavering and incontestable truth. But information is a primary mode in which the world and its peoples are governed and understood today. As such, us humans have a right to fight for data’s informatic shapes to be equitable and just.[52]
While ultimately shocking and dark in its introduction of these issues, Metric mysticism: A troll’s tale is dripping with the artist’s sharp humour, and finely honed skill at making simultaneously fantastical and logical connections between ideas and images, histories of film, literature, music and art, while weaving new and political narratives pertinent to the datafied contemporary experience.
Similarly, Tega Brain and Sam Lavigne are New York and Sydney-based collaborators who, with tongue firmly in cheek, both critique and manipulate existing tools of big data industry. The pair develop digital projects, installations and propositional models, and combine creative coding with an aesthetic and technique that Lavigne has coined as ‘scrapism’.[53] Some of their most recent collaborative projects have developed and trialled propositions for environmentally focused alternatives to data-driven revenue models, including their work in the exhibition Synthetic messenger 2021.
Initially a live performance presented on Zoom (at the height of the pandemic), Synthetic messenger is presented at ACCA as a performance documentation in the form of a multi-screen video installation. In Synthetic messenger, Brain and Lavigne explored and manipulated ‘engagement data’, which they explain is the internet and data analytics business model that elevates and controls what content we ultimately see online.[54] The artists engaged a botnet – a network of online devices running one or more bots – to click on any advertising displayed on factual news articles about climate change.[55] As a form of innovative but ultimately speculative direct action, Synthetic messenger also seeds the premise of the digital commission developed for Data Relations.
Brain and Lavigne’s projecttitled Offset is due for release online in early 2023, both as part of Data Relations and as the inaugural ACCA Digital Wing commission. Like Synthetic messenger, this workre-contextualises current practices in the data economy that advantage corporations who profit from climate change inaction, as well as those taking only superficial action. Proposing an alternative to the carbon credits model, in which users pay big businesses to offset their corporate footprint, Offset has possible practical, or ‘real-world’ outcomes that might arise from the artist-led interventions into this false economy, from flattening a stranger’s tyres to reduce carbon emissions, to a prospective business pitch to Qantas. Like all of Brain and Lavigne’s works, these projects are simultaneously sober and spoof, which gives nuance to the artists’ understanding of data as ‘a way of understanding and shaping the world.’[56]
Lauren Lee McCarthy’s projects traverse a similarly intentionally slippery engagement with proposition and realisation. The Los Angeles-based artist works in performance, software, electronics, internet, film, photography and installation, exploring what it means ‘to be truly present’, as they describe it, ‘in the midst of always on networked interfaces’[57] For McCarthy:
Data is a form of representation, of ourselves, our relationships, our lives, our reality. Like any representation it is incomplete, imperfect, subject to interpretation. I’m interested in the ways we negotiate over this representation of reality, the way we speak to each other through data.[58]
For Data Relations, McCarthy has developed a site-specific installation of two related projects Surrogate 2020– and LAUREN 2017–. Comprising an architecturally-scaled, navigable installation in ACCA’s gallery, McCarthy guides visitors through a series of videos, sculptural forms and materials that echo of interiors and processes involved in each project. LAUREN originally saw the artist inhabit the form of an artificially intelligent home device for a week at a time in the service of a series of strangers.[59] ‘Our home is the first site of cultural education,’ McCarthy explains, ‘it’s where we learn to be a person. By allowing these devices in, we outsource the formation of our identity to a virtual assistant whose values are programmed by a small, homogenous group of developers.’[60]
Both LAUREN and Surrogate are works that poetically and complexly extend McCarthy’s explorations of ‘the tensions between intimacy vs privacy, convenience vs agency they present’ in relation to increasing automation.[61] Surrogate is underpinned by an opportunity for a prospective parent to control the artist’s actions via an app with access to their biometric data.[62] While the sinister potentials of this proposition loom large, as in LAUREN, the artist tempers the escalation or intensification of such critique with an intimacy brought about by the artist’s genuine and highly considered exploration and documentation as to what this process and a relationship like this might entail. Indeed, there is a moment in one of the recordings in Surrogate in which McCarthy acknowledges that they are not entirely sure whether they are progressing a surrogate pregnancy or mounting an art project.[63]
Likewise, Machine Listening are interested in the politics and poetics of data-driven automation, the smart home device and the politics of control. Melbourne and Canberra-based Machine Listening draws on the academic, artistic, legal, and curatorial expertise of Sean Dockray, James Parker and Joel Stern. They are focused on sound, speech and listening, and their work has taken the form of exhibitions, publications, symposia, curricula, and audio projects.[64] Machine Listening remind us that ‘data is made’, by people, and for specific purposes:
Sometimes data sets are literally produced by actors performing for researchers and their machines. Other times data sets are the product of our own performances every time we wake up Alexa or upload a video to YouTube.[65]
Machine Listening’s new project After words 2022 is an eight-channel audio installation specifically commissioned for Data Relations, which takes as its starting provocation the AI home device voice prompt, or ‘wake word’.[66] To some extent the work is a speculative reverse engineering of what takes place in the training of such devices, or as Machine Listening note, the training of us as we use the device.[67] The fifteen-minute audio work balances sense and nonsense, introducing critical issues related to data in a script read by data-focused academics, alongside the production of data-driven ambient environments. Drawing on data sets developed to interpret human emotion and speech patterns, Machine Learning have then enmeshed, overlayed, and peppered these scripts with experimentations in ‘collaboration’ with the possibilities of writing ‘with, and against’ the machine learning model GPT-3, along with automation prompts.[68] With After words, Machine Listening push and pull the data-information relationship, leading the listener along the fine line between abstract absurdity and recognisable reality that has become a hallmark of contemporary machine learning outputs.
This data-information relationship also underpins Winnie Soon’s Unerasable characters 2020–22. The London and Aarhus-based artist describes data as ‘situated’. That is, that data is ‘stored, selected, modified, presented, and retrieved in a situated context, which is never complete, objective or self-explanatory.’[69] The Unerasable characters series is being shown at ACCA together for the first time. Comprising three parts developed in temporal ‘reverse’ order, Unerasable characters isa mediation on time as well as methodologies and politics of communication in a data-driven era. Each part of the Unerasable characters project includes data sets comprising social media posts collected, and made publicly available for research, as part of the Weibo censorship research archive and visualisation project ‘Weiboscope’ by Professor of Journalism and Media Studies at University of Hong Kong, Fu King-wa.[70] However, where Dr Fu’s research is focused on highlighting and analysing the censored content, by contrast Soon is more interested in the aesthetics and poetics of the material rather than the specific text or image.[71] Unerasable characters is, as Soon describes it, an exploration of data about data, or metadata, ‘like the timestamp of a social media tweet’.[72]
Soon developed the Unerasable characters seriesprogressively from the format of the desktop/online iteration (part III, 2020), to in-gallery projection (part II, 2021), to printed paper and book (part I 2022). However, although the material technology in each project seemingly also works backwards in time, the methodologies for working with the data sets have escalated in complexity; from initially presenting the data as an infinite static scroll, to activating the disappearance by timestamp of posts, and then using this content as a machine learning training data set to produce new written material. ‘The reason I am interested in metadata is because it gives a little bit more information about the context, of which we are not usually visually aware.’[73] As soon explains, Unerasable characters I-III addresses the cultural normalisation of censorship implemented through technological platforms, as well as poetically pushing the‘materiality and malleability of data.’
The projects presented in Data Relations are provocations, projections, proposals, and speculations. Poetic, spectacular, immersive and engaging, the works in this exhibition wrestle data into the realm of human senses. Data Relations will make you think about your clicks, cables, and communications, and your relationships with technology and with each other as mediated by data.
[1] In their widely cited text on big data, academic Viktor Mayer-Schönberger and journalist Kenneth Cukier seeded the term ‘datafication’ to describe the process by which an experience, an object, a human activity or part of the body is able to be transformed into a state that can be measured and then gathered for analysis. See: Viktor Mayer-Schönberger and Kenneth Cukier, Big Data: A Revolution that Will Transform How We Live, Work, and Think, John Murray Publishers, London, 2013, p.78. For the broader definition of the term, with reference to wider impacts on society by big data, see also: José van Dijck, The Datafied Society: Studying Culture through Data, Amsterdam University Press B.V., Amsterdam 2017, p.11.
[2] Simply put, a ‘data set’ is the structured collection of data points within a defined set of parameters. Each of the artists in Data Relations use and/or refer to different types of data sets, as discussed further below.
[3] Examples of significant early data-related exhibitions, and shows including major data visualisations with online data sets include, Christiane Paul (curator), Data Dynamics, Whitney Museum of American Art, New York, 2001, https://artport.whitney.org/exhibitions/past-exhibitions.shtml; Julian Stallabrass (curator), Art and Money Online, Tate, London, 2001, https://www.tate.org.uk/whats-on/tate-britain/art-now-art-and-money-online; Steve Deitz, Sarah Cook and Anthony Keindl (curators), Database Imaginary,The Walter Phillips Gallery of the Banff Centre and the Dunlop Art Gallery, Regina, Canada, 2004 (touring), https://www.blackwoodgallery.ca/program/database-imaginary.
[4] There is a tendency in data-related exhibitions towards providing glossaries, which are sometimes references, and at other times not. Rather than repeat this process, the definition of machine learning provided here draws from the particularly well-produced podcast associated with the Carnegie Museum of Art exhibition Mirror with a Memory 2021. In this podcast, Los Angeles-based artist Martine Syms provides an accessible breakdown of the terms ‘machine learning’, ‘algorithms’ and ‘artificial intelligence’. Machine learning, Syms notes, is the process by which computers are trained with datasets to help them recognise similar instances, and she likened this to the way humans might learn to recognise and remember using sets of flash cards. Algorithms, Syms continues, are the mathematical formula that humans develop to enable machines to sort, categorise, and make decisions and predictions based on the datasets from which they learn. About AI, Syms offers that at its most basic, it is ‘a computer or machine that has been trained to do something that previously required human cognition.’ Martine Syms, ‘Episode One: Biometrics’, Mirror with a Memory (podcast), Carnegie Museum of Art, Pittsburgh, 1 February 2021, https://cmoa.org/art/hillman-photography-initiative/mirror-with-a-memory/podcast/episode-one-biometrics/. For further references to ‘big data’ see the in-text references below, and footnote 10. Examples of two high profile/controversial solo exhibitions include, Jay Sanders (curator), Laura Poitras: Astro Noise, Whitney Museum of American Art, New York, 2016, https://whitney.org/exhibitions/laura-poitras; and Trevor Paglen and Kate Crawford (artists and curators), Training Humans, Milan Osservatorio: Fondazione Prada Prada, Milan, 2019-20, https://www.fondazioneprada.org/project/training-humans/. Examples of other recent projects encompassing data-related concerns include: Anna Briers (curator), Conflict in My Outlook_Don’t Be Evil, University of Queensland Art Museum, Brisbane, 2020-21, https://art-museum.uq.edu.au/dontbeevil; Deoksun Park (curator), Vertiginous Data, Museum of Modern and Contemporary Art, South Korea, 2019, https://www.mmca.go.kr/pr/newsDetail.do?menuId=01H000801000000&bdCId=201903210006717.
[5] Data for ‘social good’ is a catch-all for some of the alternative approaches to using the power of big data, other than for profit. There are now numerous university courses on Big Data for Social Good. American academics Catherine D’Ignazio and Lauren F Klein canvas a variety of names for what they describe as ‘instances of ground-up data collection, including counterdata or agonistic data collection, data activism, statactivism, and citizen science (when in the service of environmental justice)’ [original emphasis]. Catherine D’Ignazio and Lauren F Klein, Data Feminism, MIT Press, Cambridge, 2020, p.34. One high profile example in contemporary artistic practice is of course the work of artist-research collective Forensic Architecture that uses big data, surveillance, correlation and machine learning to develop analyses and material in support of non-for profit legal initiatives lodging claims of human rights abuses across the world. See for example, https://forensic-architecture.org/.
[6] See again: Steve Deitz, et. al., Database Imaginary,2004; as well as, José Luis de VicenteandOlga Subirós (curators), Big Bang Data, Centre de Cultura Contemporánia de Barcelona, Barcelona, 2014 (touring), https://www.cccb.org/en/exhibitions/file/big-bang-data/45167; Michel van Dartel (curator), Data in the 21st Century, V2_ Lab for the Unstable Media, Rotterdam, 2015-16, https://v2.nl/events/data-in-the-21st-century;Peter Weibel (curator), Open Codes: The World as a Field of Data, ZKM Center for Art and Media Karlsruhe, Germany, 2017-19, https://zkm.de/en/exhibition/2017/10/open-codes; and Troy Casey, Beck Davis, Angela Goddard, Amanda Hayman and Katherine Moline (curators), The Data Imaginary: Fears and Fantasies, Griffith University Art Museum, Brisbane, 2021 (touring), https://thedataimaginary.com; Mirror with a Memory, Carnegie Museum of Art, Pittsburgh, 2021, https://cmoa.org/art/hillman-photography-initiative/mirror-with-a-memory/, among others.
[7] For examples of the growing number of projects related to data in the context of AI, see: Understanding AI, Ars Electronica, Linz, 2019, https://ars.electronica.art/center/en/exhibitions/ai/; AI More than Human, Barbican Centre, London, 2019, https://www.barbican.org.uk/whats-on/2019/event/ai-more-than-human; Claudia Schmucklii (curator), Uncanny Valley: Being human in the age of AI, Fine Arts Museum San Francisco, 2020-21, https://www.famsf.org/exhibitions/uncanny-valley.
[8] Catherine D’Ignazio and Lauren F Klein, Data Feminism, MIT Press, Cambridge, 2020, p.14.
[9] ibid.
[10] For an examples of the breakdown of the ‘etymology’ and references to definitions of big data see: Rob Kitchin and Gavin McCardle, ‘What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets’, Big Data & Society, January–June 2016: 1–10, pp.1-3, DOI: 10.1177/2053951716631130. See also: Mayer-Schönberger and Cukier, Big Data, 2013.
[11]This topic is expanded on at length in a wide array, indeed almost all critical data studies contexts, but perhaps for brevity, see for example as initial primers: Cathy O’Neil, Weapons of Math Destruction, Penguin Random House, New York, 2016; or, Mark Andrejevic, ‘Big Data, Big Questions: The Big Data Divide’, International Journal of Communication, vol.8, 2014, pp. 1673–1689, https://ijoc.org/index.php/ijoc/article/view/2161. For a helpful summary of recent texts across this topic see also the introductions in: D’Ignazio and Klein, Data Feminism, 2020; and Wendy Hui Kyong Chen, Discriminating Data, MIT Press, Cambridge, 2022.
[12] In their exhibition Big Bang Data, José Luis de VicenteandOlga Subirós posited a starting point for this in the sixteenth century, by including a drawing documenting a database-like methodology for recounting the philosopher’s teachings by Ramon Llull, a Spanish philosopher from the Middle Ages, http://bigbangdata.cccb.org/en/arbor-scientiae-ramon-llull/.
[13] D’Ignazio and Klein, 2020, pp.76-77.
[14] For expanded engagements with data visualisation in wider art, design and social practice contexts see: Ian Milliss, (guest ed.), Artlink, ‘Data Visualisation’, issue 37:1, March 2017.
[15] Kynaston McShine (curator), Information, Museum of Modern Art, New York, 1970. See also: Chris Nash, ‘Hans Haacke and data: Infusing the banality of fact with meaning’, Artlink, 37:1, March 2017, pp.58-63. See again: Steve Deitz, et. al., Database Imaginary,2004
[16] Christiane Paul, Digital Art, Thames & Hudson, London, 2003 (second edition, 2008), pp. 175-189.
[17] Lev Manovich, ‘The Anti-Sublime Ideal in Data Art’, 2002, http://manovich.net/content/04-projects/040-data-visualisation-as-new-abstraction-and-anti-sublime/37_article_2002.pdf; and expanded on later in Lev Manovich, ‘Artistic Visualization’, Christiane Paul (ed.), A Companion to Digital Art, John Wiley & Sons, Chichester, 2016, pp. 426-444.
[18] ibid.
[19] ibid.
[20] ibid.
[21] In an Australian context, see, as a small sample: Mark Andrejevic, Automated Media, Routledge, London, 2019, (particularly the chapter on big data and ‘framelessness’); Anna Briers, Nicholas Carah & Holly Arden (eds.) Conflict in My Outlook, Perimeter and University of Queensland Art Museum, Melbourne and Brisbane, 2022; Mitchell Whitelaw, ‘Australasian data practices: Mining, scraping, mapping, hacking’, Artlink, 37:1, March 2017, pp. 18-21; and Whitelaw’s work with Katrina Sluis and Baden Pailthorpe in the Computational Culture Lab at the Australian National University, among others.
[22] Recent publications by exhibiting artists include, for example: Golan Levin and Tega Brain, Code as Creative Medium: A Handbook for Computational Art and Design, MIT Press, Cambridge, 2021; Marcel Schwierin Molnár (ed.), Zach Blas: Unknown Ideals, Sternberg Press and Edith-Russ-Haus für Media Kunst, London and Oldenburg, 2021; Geoff Cox and Winnie Soon, Aesthetic Programming a Handbook of Software Studies, Open Humanities Press, London, 2020; James Parker and Joel Stern (eds.) Eavesdropping: A Reader, City Gallery Wellington, Liquid Architecture and Melbourne Law School, University of Melbourne, Wellington and Melbourne, 2019; as well as the wealth of writing and pedagogy available via each artists academic or artist websites.
[23] Particularly in texts and projects following the release of surveillance visuals that accompanied the American National Security Agency files leaked in 2013 by former employee and whistle-blower Edward Snowden, such as: Hito Steyerl, ‘A Sea of Data: Apophenia and Pattern (Mis-)Recognition’, Duty Free Art, Verso, London and New York, 2017, pp. 47-61.
[24] Julie Freeman, Geraint Wiggins, Gavin Starks, Mark Sandler, ‘A Concise Taxonomy for Describing Data as an Art Material’, Leonardo, 51: 1, 2018, pp. 75-79, https://doi.org/10.1162/LEON_a_01414.
[25] See, for example: Zach Blas in conversation with Australian Centre for Contemporary Art, 2022, https://acca.melbourne/zach-blas-what-is-data. Blas also references the work of Alexander R. Galloway, see, for example: Galloway, ‘From Data to Information’, Culture and Communication, 22 September 2015, http://cultureandcommunication.org/galloway/from-data-to-information.
[26] Mitchell Whitelaw, ‘Art Against Information: Case Studies in Data Practice’, The Fibreculture Journal, 11, 2008, https://eleven.fibreculturejournal.org/fcj-067-art-against-information-case-studies-in-data-practice/.
[27] ibid.
[28] ibid.
[29] Data Relations exhibiting artist-research collective Machine Listening describe the adage of data as like coal or oil as ‘a common myth’, in conversation with Australian Centre for Contemporary Art, 2022, https://acca.melbourne/machine-listening-what-is-data/. See also, for example: just one part of the potted history of the proclamations of ‘data as the new oil’ since 2006 in, Michael Haupt, ‘“Data is the New Oil” – A Ludicrous Proposition’, 2 May 2016, Medium, https://medium.com/project-2030/data-is-the-new-oil-a-ludicrous-proposition-1d91bba4f294.
[30] James Bridle, New Dark Age: Technology and the end of the Future, London and New York, Verso, 2018, pp.245-248.
[31] ibid. Like Bridle, American activist and lawyer Jasmine McNeilly has also reflected on the ways in which data, as a form of asset and particularly personal or cultural data, is, not necessarily the property of those by whom it is was generated. In America, as McNeilly notes, weak data governance regulations mean that data is owned by those who first manage to mine or capture it. See: Jasmine McNealy, ‘An Ecological approach to data governance’, Data & Society (lecture), 8 January 2020, https://m.youtube.com/watch?v=hQ0PU0DRKw8.
[32] Mimi Ọnụọha, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/mimi-onuoha-what-is-data/.
[33] Zach Blas, Metric mysticism 2017–18, https://zachblas.info/works/metric-mysticism/.
[34] Winnie Soon, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/winnie-soon-what-is-data/.
[35] Tega Brain and Sam Lavigne, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/tega-brain-and-sam-lavigne-what-is-data/.
[36] Lauren Lee McCarthy, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/lauren-lee-mccarthy-what-is-data/.
[37] Machine Listening, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/machine-listening-what-is-data/.
[38] As noted above, this topic is expanded on at length in a wide array, indeed almost all critical data studies contexts, including, O’Neil, Weapons of Math Destruction, 2016; Bridle, New Dark Age, 2018; D’Ignazio and Klein, Data Feminism, 2020; and Chen, Discriminating Data, 2022, among others.
[39] See, for example, again, O’Neil, 2016; as well as Meredith Broussard, Artificial Unintelligence, The MIT Press, Cambridge, 2019; McKenzie Wark, Capital is Dead: Is This Something Worse?, Verso, New York, 2019; Shoshana Zuboff, The Age of Surveillance Capitalism, Profile Books Ltd, London, 2019; D’Ignazio and Klein, 2020; and Chen, 2022; among many more.
[40] Ulises Ali Mejias and Nick Couldry, The Costs of Connection, Stanford University Press, Stanford, 2019, for an introduction to data relations see: pp. xiii; 12; and 27.
[41] ibid., p.27.
[42] ibid., p.27.
[43] ibid. p.27.
[44] Mimi Ọnụọha, These networks in our skin 2021, https://mimiỌnụọha.com/these-networks-in-our-skin.
[45] Mimi Ọnụọha, interviewed by Natalie Kerby, ‘Becoming Data Episode 1: Data & Humanity’, Data & Society Podcast, 17 May 2021, https://listen.datasociety.net/episodes/becoming-data-data-social-life, accessed 18 May 2021.
[46] ibid.
[47] Mimi Ọnụọha, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/mimi-onuoha-what-is-data/.
[48] Mimi Ọnụọha, ‘On-Algorithmic-Violence’, GitHub, 2018, https://github.com/MimiỌnụọha/On-Algorithmic-Violence.
[49] Zach Blas, Metric mysticism 2017–18, https://zachblas.info/works/metric-mysticism/, see also: Zach Blas, ‘Lecture Performance – Zach Blas: Metric Mysticism’, e-flux, 27 January 2018, https://www.e-flux.com/live/170578/lecture-performance-zach-blas-nbsp-metric-mysticism/.
[50] Nadja Millner-Larsen in Conversation with Zach Blas, ‘Critical Correspondence’, 21 May 2019, Movement Research, https://movementresearch.org/publications/critical-correspondence/nadja-millner-larsen-in-conversation-with-zach-blas.
[51] Among other sources, a helpful introduction to Blas’s focus on Palantir, and the company’s activities is outlined in Kris Paulsen, ‘It Is Decidedly So: Icosahedron’s Oracular Intelligence in the Post-futurist Age’, Zach Blas: Unknown Ideals, Sternberg Press and Edith-Russ-Haus für Media Kunst, London and Oldenburg, 2021, pp.162-178.
[52] Zach Blas in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/zach-blas-what-is-data/.
[53] Sam Lavigne describes ‘scrapism’ as ‘the practice of web scraping for artistic, emotional, and critical ends. It combines aspects of data journalism, conceptual art, and hoarding, and offers a methodology to make sense of a world in which everything we do is mediated by internet companies.’ Read the full definition at: Sam Lavigne, ‘Scrapism’, https://scrapism.lav.io/.
[54] Tega Brain and Sam Lavigne, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/tega-brain-and-sam-lavigne-what-is-data/.
[55] Tega Brain and Sam Lavigne, Synthetic messenger 2021, http://syntheticmessenger.labr.io/.
[56] Tega Brain and Sam Lavigne, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/tega-brain-and-sam-lavigne-what-is-data/.
[57] Lauren Lee McCarthy, artist statement via https://lauren-mccarthy.com/Info.
[58] Lauren Lee McCarthy, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/lauren-lee-mccarthy-what-is-data/.
[59] Lauren Lee McCarthy, LAUREN 2017–, https://lauren-mccarthy.com/LAUREN; see also, Lauren Lee McCarthy, ‘Feeling at home: Between human and AI’, Medium (Immerse channel), 9 January 2018, https://immerse.news/feeling-at-home-between-human-and-ai-6047561e7f04.
[60] ibid.
[61] McCarthy, artist statement.
[62] Lauren Lee McCarthy, Surrogate 2020–, https://lauren-mccarthy.com/Surrogate-Info.
[63] Lauren Lee McCarthy, Surrogate: Info, https://lauren-mccarthy.com/Surrogate-Info.
[64] Sean Dockray, James Parker, Joel Stern, ‘Machine Listening, a curriculum’, 2020, https://machinelistening.exposed/curriculum/.
[65] Machine Listening, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/machine-listening-what-is-data/.
[66] Machine Listening, in correspondence with the author, 2022.
[67] Machine Listening, in correspondence with the Australian Centre for Contemporary Art, 2022.
[68] Machine Listening explain that they use an ‘auto-regressive language model trained on a data set with over 175 billion parameters. Developed by Silicon Valley venture Open AI, GPT-3 is currently considered to be the most advanced text generating artificial intelligence program, and is currently exclusively licenced to Microsoft. Machine Listening, in correspondence with the Australian Centre for Contemporary Art, 2022.
[69] Winnie Soon, in correspondence with the Australian Centre for Contemporary Art, 2022, https://acca.melbourne/winnie-soon-what-is-data/.
[70] ibid. Weibo is the primary social media platform in China. For further information and access to the data sets, see Fu King Wa, ‘Weiboscope Open Data’, 2021,https://datahub.hku.hk/articles/dataset/Weiboscope_Open_Data/16674565, and more about Fu King Wa’s project in, Fu KW, Chan CH, Chau M, ‘Assessing Censorship on Microblogs in China: Discriminatory Keyword Analysis and the Real-Name Registration Policy’, Internet Computing, 2013, 17:3, pp.42-50.
[71] ibid.
[72] ibid.
[73] Ibid.