Evidence map›Paper›PMID 42846193›Full record

ReviewAI and ethics2026

Whose responsibility? Deconstructing global responsible AI from the African lens: a review.

Damian Eke, Makuochi Nkwo, Ibifuro Robert Jaja, Muhammad Adamu, Simisola Akintoye, Michael Ugom, Sarah Kiden

Abstract readReview
In one paragraph

Review in AI and ethics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Damian EkeSchool of Computer Science, University of Nottingham, Nottingham, UK.
Makuochi NkwoSchool of Computing and Mathematical Science, Faculty of Engineering and Science, University of Greenwich, London, UK.
Ibifuro Robert JajaCentre for Philosophy and Artificial Intelligence Research {PAIR}, University of Erlangen-Nuremberg, Erlangen, Germany.
Muhammad AdamuSchool of Computer Science, University of Nottingham, Nottingham, UK.
Simisola AkintoyeNorthumbria School of Law, Northumbria University, Newcastle upon Tyne, UK.
Michael UgomCentre for Environmental Management and Green Energy (CEMAGE), University of Nigeria, Nsukka, Nigeria.
Sarah KidenSchool of Electronics and Computer Science, University of Southampton, Southampton, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper uses the theory of decoloniality to critically examine how African scholars have interrogated the dominant, global narratives surrounding responsible Artificial Intelligence (AI). Through a multivocal literature review, the paper reveals a range of critical perspectives that expose systemic exclusions and imbalances in the current responsible AI discourse. Key themes identified include the marginalisation of African epistemologies, values, and principles; the absence of representative African datasets, lack of locally grounded regulatory frameworks; the limited inclusion and or availability of African expertise and funding; and the persistent power asymmetries and coloniality embedded in global responsible AI ethics and governance. The review highlights emerging efforts towards reconstructing responsible AI from an African contextual lens. Central to this constructive turn is the proposal of a decoloniality framework that puts emphasis on African knowledge systems, reclaims agency in AI development, and reimagines responsible AI in ways that are contextually grounded, pluriversal, and socially acceptable. This review not only contributes to critical AI scholarship but also charts pathways for more inclusive and equitable AI futures on the continent.

Indexed as

AfricaDecolonialityDeconstructionEpistemic injusticeMulti-vocal literatureResponsible AI

Identifiers

PMID42846193
PMCPMC13642439

What OpenQuestion holds

Textmetadata
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.