Ethical and strategic challenges of AI weapons: A call for global action
Bibliographic record
Abstract
The intelligence of AI weapons is primarily reflected in their ability to make autonomous decisions. However, the autonomy granted to these weapons systems raises deep ethical concerns. The very concept of autonomy, originating from the Greek for "self-law," hints at machines making pivotal decisions without human guidance. Despite AI weapons' capability to execute complex computations and decision-making processes, they are still limited by their programming code. It’s difficult to make the 'black box' nature of machine learning fully interpretable or to ensure that AI systems perform as expected after deployment. These systems learn from their environment, and the real world is never as simple as the laboratory. This absence of human moral judgment is troubling and poses risks of tragic errors. Keywords: ethical challenges, strategic challenges, AI weapons, global action DOI: 10.25165/j.ijabe.20241705.9456 Citation: Bai J W, Mujumdar A S, Xiao H W. Ethical and strategic challenges of AI weapons: A call for global action. Int J Agric & Biol Eng, 2024; 17(5): 293-294.
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.
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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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".