ReviewAI & society2026
Decoloniality impact assessment for AI.
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.
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.
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.
Who cites it
1 citing paper in PubMed.
- Whose responsibility? Deconstructing global responsible AI from the African lens: a review.AI and ethics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.