Evidence map›Paper›PMID 35110627›Full record

ArticleScientific reports2022

Artificial intelligence development races in heterogeneous settings.

Theodor Cimpeanu, Francisco C Santos, Luís Moniz Pereira, Tom Lenaerts, The Anh Han

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
10.3field-weighted citation impact, top 2% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. 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

5 authors at 4 institutions in 4 countries.

Theodor CimpeanuSchool of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, TS1 3BA, UK.
Francisco C SantosINESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisbon , Portugal.
Luís Moniz PereiraNOVA Laboratory for Computer Science and Informatics (NOVA-LINCS), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal.ORCID 0000-0001-7880-4322
Tom LenaertsMachine Learning Group, Université Libre de Bruxelles, 1050, Brussels, Belgium.
The Anh HanSchool of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, TS1 3BA, UK. T.Han@tees.ac.uk.
Teesside University · GBInstituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento · PTUniversidade Nova de Lisboa · PTUniversité Libre de Bruxelles · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues. With the great benefits promised from being able to first supply such technologies, safety precautions and societal consequences might be ignored or shortchanged in exchange for speeding up the development, therefore engendering a racing narrative among the developers. Starting from a game-theoretical model describing an idealised technology race in a fully connected world of players, here we investigate how different interaction structures among race participants can alter collective choices and requirements for regulatory actions. Our findings indicate that, when participants portray a strong diversity in terms of connections and peer-influence (e.g., when scale-free networks shape interactions among parties), the conflicts that exist in homogeneous settings are significantly reduced, thereby lessening the need for regulatory actions. Furthermore, our results suggest that technology governance and regulation may profit from the world's patent heterogeneity and inequality among firms and nations, so as to enable the design and implementation of meticulous interventions on a minority of participants, which is capable of influencing an entire population towards an ethical and sustainable use of advanced technologies.

Identifiers

PMID35110627
PMCPMC8810789
OpenAlexW3213443313

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.