THE ALGORITHMIC MODERATION OF SEXUAL EXPRESSION: PORNHUB, PAYMENT PROCESSORS AND CSAM
Bibliographic record
Abstract
Pornography platforms are increasingly required by payment processor business partners to mitigate harm in their content management systems through algorithmic moderation. Demands that adult merchants incorporate these tools are not proportional to instances of harmful content, but a response to the widespread conflation of pornography with harm and risk online. This paper explores co-governance by payment processors calling for algorithmic tools through the case of Pornhub, asking: what standards are required by financial firms, how are these enforced on platforms, and what effects does this arrangement have on porn content? I open with key context regarding the deplatforming of sex, antiporn campaigning and constructions of harm through 'reputational risk’. Following this, I detail financial firms infrastructural influence in platform co-governance. Next, a close reading of adult merchant terms identifies specific clauses calling for algorithmic moderation. Concluding this issue mapping, I provide a taxonomy of moderation tools in place on Pornhub. I close with an issue discussion to consider AI's positioning as a regulatory solution, CSAM data ethics, moderator labour, and the many technical problems obscured by promises of safety through automated content management systems. The resulting review of algorithmic measures enforced by financial firms offers a detailed case of the opaque governance conditions imperilling sexual expression across porn platforms.
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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 | 0.015 | 0.028 |
| 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 | 0.045 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".