Ethics and Efficacy of Unsolicited Anti-Trafficking SMS Outreach
Bibliographic record
Abstract
The sex industry exists on a continuum based on the degree of work autonomy present in one's labor conditions: a high degree of autonomy exists on one side of the continuum where certain independent sex workers have a great deal of agency, while much less autonomy exists on the other side, where sex is traded under conditions of human trafficking. Various organizations across North America perform outreach to sex workers to offer assistance in the form of services (e.g., healthcare, financial assistance, housing) as well as prayer and intervention. Increasingly, technology is used to look for trafficking victims and/or facilitate the provision of assistance or services, for example through scraping and parsing sex industry workers' advertisements into a database of contact information that can be used by outreach organizations. However, little is known about the efficacy of anti-trafficking outreach technology, nor the potential risks of using such technology to identify and contact the highly stigmatized and marginalized population of those working in the sex industry. In this work, we investigate the use, context, benefits, and harms of an anti-trafficking technology platform via qualitative interviews with multiple stakeholders: the technology developers (n=6), organizations that use the technology (n=17), and sex industry workers who have been contacted or wish to be contacted (n=24). Our findings illustrate misalignment between developers, users of the platform, and sex industry workers they are attempting to assist. In their current state, anti-trafficking outreach tools such as the one we investigate are ineffective and, at best, serve as a mechanism for spam and, at worst, scale and exacerbate harm against the population they aim to serve. We conclude with a discussion of best practices -- and the feasibility of their implementation -- for technology-facilitated outreach efforts to minimize risk or harm to sex industry workers while efficiently providing needed services.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".