Evidence map›Paper›PMID 42040827›Full record

ReviewAI & society2026

Decoloniality impact assessment for AI.

Damian Eke, Ricardo Chavarriaga, Bernd Stahl

Abstract readReview
In one paragraph

Review in AI & society, 2026. 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. 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.

Damian EkeSchool of Computer Science, University of Nottingham, Nottingham, UK.
Ricardo ChavarriagaCentre for Artificial Intelligence, Zurich University of Applied Sciences, Winterthur, Switzerland.
Bernd StahlSchool of Computer Science, University of Nottingham, Nottingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last decade, several organisations, and national and international agencies have developed impact assessments (IAs) to mitigate the risks and impact of AI systems as well as to promote responsible, just and trustworthy design, development and deployment. However, through a critical review of current AI IAs, we identify the failure of these IAs to address fundamental questions regarding who defines problems, whose knowledge is valued, and who truly benefits from AI innovation or generally what we term the 'coloniality problem'. Developed primarily within Global North normative frameworks, these IAs risk perpetuating the very inequalities they aim to address by neglecting Global South perspectives and the extractive logic underpinning data practices. Thus, we propose a novel approach: Decoloniality Impact Assessment (DIA) as a critical, context-sensitive evaluative approach that assesses AI systems in relation to their inherent colonial legacies, global power asymmetries, and epistemic injustices. It moves beyond traditional ethical frameworks by interrogating how the AI innovation lifecycle and practices reinforce structural inequalities, marginalise local knowledge systems, and perpetuate exploitative systems. The paper advocates for an AI innovation lifecycle approach to DIA, recognising that coloniality manifests at every stage of AI development, from ideation to deployment. DIA is not a new impact assessment framework but an approach that can be integrated into already existing frameworks such as the Council of Europe's HUDERIA framework. It is a call to reframe AI innovation in a way that technological futures are rooted in justice, pluriversality, and sovereignty.

Indexed as

AIColonialityDecolonialityEthicsGlobal SouthImpact assessmentResponsibility

Identifiers

PMID42040827
PMCPMC13102833

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.