Autonomous Weapons Systems: Taking the Human out of International Humanitarian Law
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
Once confined to science fiction, killer robots will soon be a reality. Both the USA and the UK are currently developing weapons systems that may be capable of autonomously targeting and killing enemy combatants within the next 25 years. According to Additional Protocol I to the Geneva Convention and customary international law, weapons systems must be capable of operating within the principles of International Humanitarian Law (IHL). This paper will demonstrate that without significant restrictions on the use of autonomous weapons systems (AWS) or the creation of a new legal framework, the use of AWS is problematic. First, there are legitimate concerns that AWS are, by their nature, incapable of adhering to IHL principles. Second, there is a more fundamental problem: the principles of IHL are actually insufficient to address the unique concerns regarding AWS. Finally, the solutions proposed by proponents of AWS do not sufficiently address these concerns. A legal solution beyond the general principles of IHL must be developed.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it