Advancing Human Rights-by-Design in the Dual-Use Technology Industry
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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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 | 0.071 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.089 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".