Bibliographic record
Abstract
Abstract As artificial intelligence (AI) plays an increasing role in operations on battlefields, we should consider how it might also be used in the strategic decisions that happen before a military operation even occurs. One such critical decision that nations must make is whether to use armed force. There is often only a small group of political and military leaders involved in this decision-making process. Top military commanders typically play an important role in these deliberations around whether to use force. These commanders are relied upon for their expertise. They provide information and guidance about the military options available and the potential outcomes of those actions. This article asks two questions: (1) how do military commanders make these judgements? and (2) how might AI be used to assist them in their critical decision-making processes? To address the first, I draw on existing literature from psychology, philosophy, and military organizations themselves. To address the second, I explore how AI might augment the judgment and reasoning of commanders deliberating over the use of force. While there is already a robust body of work exploring the risks of using AI-driven decision-support systems, this article focuses on the opportunities, while keeping those risks firmly in view.
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 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 | 0.001 | 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".