New Technologies and Deterrence: Artificial Intelligence and Adversarial Behaviour
Bibliographic record
Abstract
Abstract Offering a critical synthesis of extant insights into technological developments in AI and their potential ramifications for international relations and deterrence postures, this chapter argues that AI risks influencing military deterrence and coercion in unique ways: it may alter cost-benefit calculations by removing the fog of war, by superficially imposing rationality on political decisions, and by diminishing the human cost of military engagement. It may recalibrate the balance between offensive and defensive measures, tipping the scales in favour of pre-emption, and undermine existing assumptions imbedded in both conventional and nuclear deterrence. AI might altogether remove human emotions and eliminate other biological limitations from the practice of coercion. It may provide users the ability to collect, synthesize, and act upon real-time intelligence from several disparate sources, augmenting the certainty and severity of punishment strategies, both in theatre and online, compressing the distance between intelligence, political decisions, and coercive action. As a result, AI may quicken the overall pace of action across all domains of coercion, in conflict, crisis, and war, and within the related subfields of national security, counterterrorism, counter-crime, and counter-espionage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".