Human-AI cognitive teaming: using AI to support state-level decision making on the resort to force
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
Artificial Intelligence (AI) and machine learning (ML) are rapidly evolving and have already had major impacts on military capabilities in the battlefield, making new kinds of tools and tactics available. A less examined area of application for AI in a military context, however, is its impact on human strategic decision making. This article focuses on the more subtle cognitive influences of AI and how they can be strategically deployed to aid decision making around the state-level resort to force, in particular. I will argue that AI-driven technologies can be used to improve certain critical cognitive resources (e.g. memory, planning, mind-modelling, etc.) of decision makers, thereby providing valuable strategic advantages to those actors who use them successfully. At the same time, I will also caution against the risks of human decision makers becoming overly reliant on AI-support systems. Both the potential advantages and risks are areas that demand further study and consideration.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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