Artificial intelligence and psychological: Propaganda operations in the context of threat to national security
Bibliographic record
Abstract
The key characteristic of international relations is their anarchy and in modern conditions this is manifested by the continuous performance of psychological-propaganda operations (PsyOp) by some actors against others. PsyOp represent the first stage in the preparation and implementation of a hybrid war, but they can also be an end in themselves. Over time, they have become an indispensable means of ensuring national security. National security is ensured by eliminating or relativizing the conflicting interests of rivals (enemies) against whom PsyOp are directed. A new moment in the application of this concept is the development and (mis) use of artificial intelligence (AI). The capacities of artificial intelligence for designing and implementing PsyOp far exceed human potential. It can introduce international relations into a stage of constant and permanent conflicts by carrying out continuous psychological-propaganda operations and starting hybrid wars that will never end. Another danger lies in the claim of the creators of AI that the AI has its own logic, and because of this, in the future, it will depend less and less on given inputs. In an anarchic environment, AI can independently induce and generate wars by conducting unpredictable PsyOp. The author's conclusion is that the combination of traditional anarchy and new technology worsens the national security of states, but indirectly also global security, and therefore it is necessary to think about different ways of limiting the use of AI in international relations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.039 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".