Evidence map›Paper›PMID 35035113›Full record

ArticleAI & society2022

Optimising peace through a Universal Global Peace Treaty to constrain the risk of war from a militarised artificial superintelligence.

Elias G Carayannis, John Draper

Abstract read
In one paragraph

Article in AI & society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Elias G CarayannisGeorge Washington University (European Union Research Center), Washington, DC USA.ORCID 0000-0003-2348-4311
John DraperNonkilling Economics and Business Research Committee, Center for Global Nonkilling, Honolulu, HI USA.ORCID 0000-0002-3626-533X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article argues that an artificial superintelligence (ASI) emerging in a world where war is still normalised constitutes a catastrophic existential risk, either because the ASI might be employed by a nation-state to war for global domination, i.e., ASI-enabled warfare, or because the ASI wars on behalf of itself to establish global domination, i.e., ASI-directed warfare. Presently, few states declare war or even war on each other, in part due to the 1945 UN Charter, which states Member States should "refrain in their international relations from the threat or use of force", while allowing for UN Security Council-endorsed military measures and self-defense. As UN Member States no longer declare war on each other, instead, only 'international armed conflicts' occur. However, costly interstate conflicts, both hot and cold and tantamount to wars, still take place. Further, a New Cold War between AI superpowers looms. An ASI-directed/enabled future conflict could trigger total war, including nuclear conflict, and is therefore high risk. Via conforming instrumentalism, an international relations theory, we advocate risk reduction by optimising peace through a Universal Global Peace Treaty (UGPT), contributing towards the ending of existing wars and prevention of future wars, as well as a Cyberweapons and Artificial Intelligence Convention. This strategy could influence state actors, including those developing ASIs, or an agential ASI, particularly if it values conforming instrumentalism and peace. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00146-021-01382-y.

Indexed as

AI arms raceArtificial superintelligenceConforming instrumentalismExistential riskInternational relationsPeace

Identifiers

PMID35035113
PMCPMC8748529

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.