Evidence map›Paper›PMID 40529642›Full record

ReviewFrontiers in artificial intelligence2025

Artificial intelligence, complexity, and systemic resilience in global governance.

Andrés Ilcic, Miguel Fuentes, Diego Lawler

Abstract readReview
In one paragraph

Review in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Data quality challenges of AIGC application in smart agriculture.Frontiers in artificial intelligence · 2025
    Review
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

3 authors.

Andrés IlcicFacultad de Filosofía y Humanidades, Universidad Nacional de Córdoba, Córdoba, Argentina.
Miguel FuentesInstituto de Investigaciones Filosóficas, SADAF-CONICET, Buenos Aires, Argentina.
Diego LawlerInstituto de Investigaciones Filosóficas, SADAF-CONICET, Buenos Aires, Argentina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) is reshaping international governance, presenting opportunities to enhance systemic resilience while posing significant ethical, social, and geopolitical challenges. This paper argues that complexity science offers a valuable framework for navigating AI's integration into global governance systems. We analyze AI's dual capacity as both a transformative tool for improving decision-making, resource allocation, and crisis management, and as a disruptive force introducing risks like data bias, exacerbated inequalities, and governance gaps. By framing resilience as a crucial, boundary concept bridging disciplines and practice, we advocate for adaptive, inclusive governance models capable of managing the inherent uncertainties of AI-driven complex socio-technical systems. Integrating complexity insights with principles like institutional modularity and robust stakeholder collaboration is vital for fostering equity, accountability, and sustainability. This study proposes a conceptual approach aiming to align technological innovation with societal values, ensuring AI deployment contributes to a more resilient and equitable global future, while at the same time it proposes complexity as a boundary concept to bridge the gap between governance literature and philosophy of science and technology.

Indexed as

adaptive governanceartificial intelligence (AI)complexity scienceethical AIinternational governancescience diplomacysocio-technical systemssystemic resilience

Identifiers

PMID40529642
PMCPMC12171231

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.