Artificial Intelligence and Research on the Consequences of War: Thematic overview of eight papers and preparation for project implementation

Authors

  • Dmitry Nikolaenko Editor-in-Chief, Pollution and Diseases Author

DOI:

https://doi.org/10.66659/4dhdjx86

Keywords:

artificial intelligence; scientist–AI system; long-term consequences of war; military environmental contamination; Vietnamese archives; Scientific Recognition Architecture; hypothesis generation; evidence status; uncertainty; field verification; interdisciplinary collaboration; multilingual research training.

Abstract

This overview synthesises eight papers into a research programme for using artificial intelligence to investigate the environmental consequences of war. It proposes a scientist–AI system in which expert judgement, explicit responsibility, hypothesis generation, evidence classification, and field verification form an integrated cycle of inquiry. Scientific Recognition Architecture supports the progressive clarification of research objects while keeping explanations open to correction. A proposed pilot using Vietnamese archives provides the setting: a limited documentary corpus would be examined to reconstruct candidate locations and periods for environmental investigation. Archival statements, interpretations, hypotheses, and observations retain distinct evidential status; historical records alone cannot establish present contamination or health effects. AI assists translation, comparison, and the development of alternative explanations, while consequential claims require source checking and specialist assessment. The programme preserves the grounds for decisions, rejected hypotheses, unresolved questions, and revisions across changing projects and participants. Practical online training will prepare an international team to apply these procedures using shared research materials. Participation in native or strongest working languages will be supported through checked translation and common terminology. The pilot will assess whether this organisation improves research questions, error correction, and justified verification priorities.

References

[1] Laboratory of Military Ecotones. 2026. The Scientist–AI System: Rethinking Research on War-Related Environmental Contamination. Pollution and Diseases. DOI 10.66659/dz0jgs38

https://pollution-diseases-ojs.org/index.php/pd/article/view/74

[2] Laboratory of Information Systems. 2026. Who Is the Author and Why Is the Problem Still Unsolved? AI and the Failure of Super-Normal Science. Pollution and Diseases. DOI 10.66659/svj1dy10

https://pollution-diseases-ojs.org/index.php/pd/article/view/75

[3] Laboratory of Information Systems. 2026. Responsibility to the Problem: An Explicit Standard for the Expert–AI System. Pollution and Diseases. DOI 10.66659/gc495972

https://pollution-diseases-ojs.org/index.php/pd/article/view/77

[4] Laboratory of Military Ecotones. 2026. AI-Assisted Hypothesis Generation: From Fragmented Manifestations to the Morphology of War Contamination. Pollution and Diseases. DOI 10.66659/bap6zf89

https://pollution-diseases-ojs.org/index.php/pd/article/view/76

[5] Laboratory of Information Systems. 2026. Evidence Status and Pathological Uncertainty: A Working Standard for War Contamination Information. Pollution and Diseases. DOI 10.66659/z0wwf379

https://pollution-diseases-ojs.org/index.php/pd/article/view/78

[6] Laboratory of Military Ecotones. 2026. Field Verification in the Scientist–AI System: From Pathological Uncertainty to Quality Data. Pollution and Diseases. DOI 10.66659/2qwv2037

https://pollution-diseases-ojs.org/index.php/pd/article/view/79

[7] Nikolaenko, Dmitry. 2026. The Scientist–AI Research System: An Integrated Working Standard for War-Related Environmental Contamination. Pollution and Diseases. DOI 10.66659/1cabcy23

https://pollution-diseases-ojs.org/index.php/pd/article/view/80

[8] Nikolaenko, Dmitry. 2026. From Scientific Recognition to Scientific Explanation: Organising Investigation of Long-Term Military Environmental Contamination. Pollution and Diseases. DOI 10.66659/db5ekw49

https://pollution-diseases-ojs.org/index.php/pd/article/view/58

[9] Vu Thi Phung, Vu Dinh Phong, and Nguyen Van Ngoc. 2026. Archival records at National Archives Center III on the use of chemical agents during the Vietnam War: value and accessibility. Pollution and Diseases. DOI 10.66659/xnkh0w22

https://pollution-diseases-ojs.org/index.php/pd/article/view/99

[10] Cu Thi Dung and Vu Dinh Phong. 2026. Archival sources of the Government of the Republic of Vietnam at National Archives Center II on chemical defoliants during the Vietnam War. Pollution and Diseases. DOI 10.66659/zdcj2c76

https://pollution-diseases-ojs.org/index.php/pd/article/view/100

[11] Nikolaenko, Dmitry. 2026. Scientific Recognition Architecture. Operational Specification Version 1.0. Pollution and Diseases. DOI 10.66659/q52haz36

https://pollution-diseases-ojs.org/index.php/pd/article/view/61

[12] UNESCO. Coalition for Linguistic Diversity in Artificial Intelligence. Global AI Ethics and Governance Observatory. Accessed 11 October 2026.

https://www.unesco.org/ethics-ai/en/coalition-linguistic-diversity-artificial-intelligence

Published

2026-10-30

How to Cite

Nikolaenko, Dmitry. 2026. “Artificial Intelligence and Research on the Consequences of War: Thematic Overview of Eight Papers and Preparation for Project Implementation”. Pollution and Diseases, October. https://doi.org/10.66659/4dhdjx86.

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