Analyzing Technosolutionism in Synthetic Face Data Companies
Bibliographic record
Abstract
As long as facial recognition technologies (FRT) have existed, they have struggled with gender and racial bias–and thus, also with barriers in critique and regulation, leading to limited product efficacy and growth. Such biases exist due to training datasets lacking demographic diversity, which leads to misclassification of underrepresented groups in various applications of FRT in society [4]. Robust training datasets, however, require appropriate protocols for obtaining and handling human data, and are thus costly and time-consuming to create [1] [3]. In response, FRT companies have turned to image-based synthetic data generation methods as a proposed solution, claiming it enhances fairness while sidestepping ethical concerns related to data privacy and consent [1] [3] [8]. Though synthetic data is framed as a superior, even moral, alternative to real human data, Whitney and Norman (2024) argue that these claims are often without sufficient scrutiny of ethical and technical limitations, “appearing to resolve valid criticism about a dataset's distribution and representation … in a way that is superficial” [9].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".