The Future of Digital Feminism: Surviving Surveillance, Misinformation, and Machine Learning Misogyny
Bibliographic record
Abstract
This article introduces the concept of machine learning misogyny to describe how emerging computational systems reproduce, amplify, and legitimize gender-based discrimination. We explore the digital afterlife of #MeToo to examine how feminist storytelling is fragmented, co-opted, and erased in an era of surveillance capitalism and reactionary backlash. The paper suggests that as digital infrastructures increasingly determine which voices are heard, believed, and silenced, feminist discourse is being reshaped by forces beyond traditional activism and backlash. To make this argument, we analyze case studies of machine learning misogyny, including AI companions, pro-natalist reproductive tech, and content moderation tools, to illustrate the resurgence of gendered and racialized control. Through critiques of platform governance and case studies of technological co-optation, this article asks: what forms of feminist resistance remain possible in an era of digital precarity? Drawing on scholars, activists, and speculative futures, we propose feminist reimagining rooted in consent, care, and coalition. Ultimately, we argue that feminist survival in the digital age requires resisting not only institutional erasure but also the seductions of surface-level technological reform.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".