International Humanitarian Law and Artificial Intelligence: A Canadian Perspective
Bibliographic record
Abstract
Artificial Intelligence (AI) is one of the most remarkable achievements in the technology world. AI can be used dually by both civilians and combatants, serving with both beneficial and harmful aims. In the military realm, by empowering military systems to perform most warfare tasks without human involvement, AI developments have changed the capacity of militaries to conduct complex operations with heightened legal implications. Accordingly, it is vital to consider the consequences emanating from its use in military operations. International Humanitarian Law (IHL), also known as the laws of war, or the Law of Armed Conflict (LOAC), is a set of rules which regulates armed conflict between States, as well as civil wars. IHL protects people who are not involved or have ceased participating in hostilities and restricts the means and methods of war. While capabilities of new means of military AI continue to advance at incredible rates, on an international level, IHL principles should be revisited to account for the new reality in military operations. Additionally, on a national level, the impacts of military AI developments on military power for international competition have attracted the attention of national authorities. Therefore, studying both international and national pathways will be necessary as the first step toward promoting transparency in legal rules. Ultimately, central to my research is analyzing the Canadian perspective on IHL and the military use of AI at both national and international levels. Using a comparative approach with the American perspective, I conclude that if Canada develops more cohesive policies on the new military use of AI, it could become a legal leader in this realm.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".