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Tactics&Practice #15: (Un)real Data – Real Effects
How is reality shaped by data? How can the act of purposely creating data provide agency within data-driven systems? Does this act open a space for action in a data-driven society where opting out is no longer an option? Where in such a society is the space for our intervention in the world?
For the 15th edition of Tactics&Practice, Aksioma’s discursive programme focusing on contemporary investigative art, society and new technologies, !Mediengruppe Bitnik (Carmen Weisskopf and Domagoj Smoljo) in collaboration with Janez Fakin Janša curates (Un)real Data – Real Effects, a series of events, activities and critical reflections that explores how the ambiguous quality of data can be used as a tool to produce real-world outcomes. The programme will take place in Ljubljana, Slovenia, in the first half of 2024 in various venues including Kino Šiška, the Aksioma Project Space, the Academy of Fine Arts and Design and Slovenska kinoteka and includes a kick-off conference, a series of exhibitions and performances, various workshops and artist talks, a new podcast series and the publication of a book with commissioned texts.
The transition towards a data-driven society requires that all the technologies we use essentially become part of a networked data-gathering infrastructure. Automated data collection has become an intrinsic component of most technologies which means that most of our interactions with technology today generate data. Data is captured, recorded, analysed, displayed and presented as reality. Our realities are thus becoming increasingly shaped by abstraction based on probabilities and patterns that are tailored to specific needs. The balance between “unfiltered” experience and experiences mediated by algorithmically processed data is shifting rapidly towards the latter. We shop online, guided by the product ratings of others. We are offered car insurance, home insurance and jobs only after being assessed by algorithms for risks based on previous data. Our news and social media feeds are tailored according to our previous likes and views. We are given access to jobs, to travel, to education based on personal data. In work settings, we are constantly monitored by devices and software: the use of workplace surveillance has exploded in recent years and bossware is assessing productivity by counting how many clicks, how many keystrokes we are making, what web pages we are surfing and how much we are contributing to chats and emails. Are we clicking fast enough? Look engaged enough in the video call? Are we interacting enough in the work chat? When productivity is rated by algorithms based on quantified data and not work results, how does that change the way we work?
Considering how data is generated in such systems, Wendy Chun concludes that a change of state is necessary, as things must be in motion in order to be recorded and become data. She goes on to say that “people who engage in heavily captured activity have a certain freedom, namely, free creation within a system of rules. They can optimise their actions, so that their effort is decreased or their recorded productivity is increased; they can become more rather than less skilful. In more cynical colloquial terms, users can game the system.” What is needed is an intervention in the “lively data” itself, as Deborah Lupton calls the constant generation of a mass of digital data that becomes part of everyday life.
Within this emerging setting, the programme (Un)real Data – Real Effects looks at how producing specific data can become a means to intervene into data-driven systems. The practice of “unreal-ing data” makes use of the ambiguous quality of data so that it does not describe the world with data but instead strategically produces the “right” data to provoke specific outcomes. (Un)real Data – Real Effects looks at how this ambiguity can become an opportunity to generate certain views on the world. Can we produce the data to appear productive to bossware, maybe even without actually working? Do we use automated mouse movers to appear productive and busy? How do we need to tailor our personal data to increase access to jobs? “Unreal-ing data” means to first understand the data-processing system we are interacting with and then to deliberately create, modify or interpret data not to conform to the world, but to transform it. Can we reframe the lack of representational quality of a data set as a feature and not a bug?
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