Bibliographic record
Abstract
On April 22, 2013, organizations across the world banded together to launch the Campaign to Stop Killer Robots. Advocates called for a ban on fully autonomous weapons (FAWs), robotic systems that can “choose and fire on targets on their own, without any human intervention.” Though no such weapon has been fully developed, the campaign has gained momentum and attracted the support of international bodies, activists, and scientists. In May 2014, just a year after the campaign began, the United Nations Convention on Certain Conventional Weapons met to debate whether a ban on FAWs is warranted. In pressing their case, activists cited the Ottawa Treaty, which banned virtually all anti-personnel landmines, as a model for full-scale prohibition.This Comment takes a different lesson from landmines. Drawing on the 1996 Amended Protocol on the use of landmines and other case studies in international law, I argue that regulation, rather than an outright ban, would likely be more effective in ensuring that FAWs comply with international law. This argument begins from the premise that the best approach to FAWs is the one most likely to reduce human suffering. I contend that FAWs are amenable to regulation and that, as a practical matter, regulation is more likely than a ban to induce compliance from countries such as the United States, China, and Russia. Ultimately, I argue that in regulating these weapons systems, nations may well be able to create an administrable legal regime for a new technology of war.The Comment proceeds in three Parts. Part I defines FAWs and introduces the legal and ethical issues surrounding these weapons. Part II draws on the history of attempts to regulate weapons systems, including landmines, to explain why regulation is the correct response to FAWs. Part III develops a framework based on the Amended Protocol to guide the use of FAWs. Though it is difficult to develop standards for such novel weapons, the momentum around a preemptive ban makes it important to consider whether regulation might instead be an effective response to FAWs. By demonstrating that existing frameworks are capable of regulating FAWs, this Comment aims to integrate FAWs into current debates in international law and to dispel the notion that these weapons raise wholly unique legal challenges.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".