Advancing Human Rights-by-Design in the Dual-Use Technology Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is no secret that technology companies have greased the wheels for human rights abuses around the world — backed by a global web of private sector support and investment that has yielded significant financial returns. For example, the University of Toronto's Citizen Lab recently published research analyzing the use of Internet filtering technology developed by Canadian company Netsweeper in ten countries globally — Afghanistan, Bahrain, India, Kuwait, Pakistan, Qatar, Somalia, Sudan, United Arab Emirates, and Yemen — and concluded these uses likely violated international human rights law. Products like Netsweeper’s Internet filtering systems are often referred to as "dual use" technologies: though they may serve legitimate societal objectives in some cases, they also used to undermine human rights like freedom of expression and privacy. Yet Netsweeper is but one example among a growing number of such dual-use tech companies, within a wider and complex cyber security industry, prepared to facilitate mass censorship and surveillance — and increasingly doing so with the financial backing of specialized and powerful investment firms. This paper cites this and other examples to help document this now billion dollar worldwide market and offers ideas and proposals to help clean it up via stronger human rights norms — including human rights-by-design for dual use technologies — among all stakeholders in the cyber security industry: from governments, to businesses, to their employees and shareholders, to industry associations, and the private investment firms funding it all.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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 it