Artificial intelligence and national defence: A strategic foresight analysis
Bibliographic record
Abstract
Strategic foresight can help address long-term uncertainties by offering insights into the potential impact of artificial intelligence (AI) on national security. This analysis highlights the value of qualitative tools in exploring a variety of future scenarios related to breakthroughs in AI. This investigation examines how strategic foresight is changing in Canada and other Five Eyes (plus one) nations - the United States, the United Kingdom, Australia, New Zealand and the Netherlands - using horizon scanning and scenario planning to improve security policies. Important observations centre on the dual nature of AI, exploring the difficulties presented by deepfake technology and cyberthreats while emphasizing the need for preventative regulatory actions to protect democratic institutions and national security. Various illustrative scenarios highlight the risks associated with unbridled AI capabilities, including the problem of incremental approaches, showcasing different degrees of AI integration for defence. Robust legislative frameworks and international cooperation are essential to control AI's impact, and strategic foresight provides a critical instrument to navigate upcoming possibilities and challenges in defence and security.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".