Algorithmic Anxiety in Contemporary Art: A Kierkegaardian Inquiry into the Imaginary of Possibility
Bibliographic record
Abstract
las and Ilse Twigt, who showed me the ropes and taught me how to keep moving in the ring of life .For you, I bow .I am deeply indebted for A long time, Artistic engAgement with Algorithms wAs mArgin-Al in contemPorAry Art .Over the past eight years, however, a growing number of artists and critical practitioners have become engaged with algorithms, resulting in algorithmic theatre, bot art, and algorithmic media and performance art of various kinds, which thematise the dissemination and deployment of algorithms in everyday life .The numerous art exhibitions that have been curated over the past years in art institutions, at festivals, in galleries and at conferences-both large and small-in Europe, the Americas, Canada, and in China, reflect this rising prominence of algorithmic art .These exhibitions aim at imagining, representing and narrativising aspects of what is called algorithmic culture: for instance, in exhibitions that address the modulation of behaviour and algorithmic governance; shows on algorithmic capitalism and data surveillance; shows on self-quantification; as well as shows on information technology and cybernetic culture and human and machine relations in general .Indeed, one might say, in the spirit of Langdon Winner, that 'algorithm' is a word whose time has come .If theorists of media and technology are to be believed, we live in an 'algorithmic culture' (Galloway 2006; Striphas 2015; Dourish 2016) .Algorithms sort, search, recommend, filter, recognise, prioritise, predict and decide on matters in a range of fields .They are embedded in high-frequency trading in the financial markets and in predicting crime rates through data profiling, for instance .They are deployed to analyse traffic, to detect autoimmune diseases, to recognise faces, and to detect copyright infringements .Mundane aspects of our lives, such as work, travel, play, consumption, dating, friendships, and shopping are also, in part, delegated to algorithms; they've come to play a role in the production of knowledge, in security systems, in the partners we choose, the news and information we receive (or not), the politicians we vote for,
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".