PREDICTIONS OF THE SELF: AI AND THE POLITICAL ECONOMY OF SUBJECTIVATION
Bibliographic record
Abstract
The recent widespread availability of Artificial Intelligence (AI) technology and the extensive records of human activities and behaviour in digital format present serious challenges related to how individuals construct their own identities and social relations. AI systems datafy our body and our sense of self, producing a new cartography of biopower (Foucault, 1982) and a new form of the political economy of subjectivation (Langlois & Elmer, 2019) that treats individuals as objects from which raw material is extracted to produce predictive models that act as our data doubles (Haggerty & Ericson, 2000). Issues such as algorithmic social biases (Bolukbasi et al., 2016), the idealized and pragmatic economic uses of AI (Srnicek, 2017), and the consequent reproduction of already existing power structures by predictive models (Crawford, 2021) have been problematized in the literature. This paper asks what kinds of data and labour mobilization occur in and around the production of predictive models: What political economy and socio-technical conditions are involved in the production of AI? How do these conditions produce predictive models that shape our sense of self and identity? Focusing on Kaggle, a platform for crowdsourcing AI development, I use digital methods and a software studies approach to examine the practices of the data science community on three high-profile machine learning projects and conclude by arguing that machine learning has been thought of and developed as a prediction of the self in order to prescribe individual behaviour to fulfill specific economic conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.057 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".