Deep acting attraction: Predation, masculinity and erotic labour in algorithmic romance
Bibliographic record
Abstract
The present paper examines the emotional labour of love in algorithmic romance, as represented by Replika, the world’s most popular artificial intelligence companion application, and its implications for ethical artificial intelligence development through the conceptual frame of deep acting . Emotional labour, the theoretical umbrella under which deep acting falls, is introduced as a scope through which to review literature on Replika. Then, the paper looks to Plato’s Alcibiades to assay elements of algorithmic romance, with three suggested features of import: predation, masculinity and erotic labour. The proffered elements are then applied to Replika through the conceptual framework of deep acting, identifying ways in which algorithms overwrite the romantic self and other, suggesting a co-opting of the human body that may extend to other algorithmic dating applications. The article concludes with implications for ethical artificial intelligence development in view of the discussed exploitation of emotional labour.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".