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arxiv: 2604.06219 · v1 · submitted 2026-03-23 · 💻 cs.CY · cs.AI

From experimentation to engagement: on the paradox of participatory AI and power in contexts of forced displacement and humanitarian crises

Stella Suge (Executive Director , FilmAid Kenya) , Sarah W. Spencer , Nyalleng Moorosi (Senior Researcher , The Distributed AI Research Institute (DAIR)) , Helen McElhinney (Executive Director , The CDAC Network) , Geoff Loane (Chair
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Sue Black (Professor of Computer Science Technology Evangelist Durham University)
This is my paper

Pith reviewed 2026-05-15 00:47 UTC · model grok-4.3

classification 💻 cs.CY cs.AI
keywords participatory AIhumanitarian crisesforced displacementpower dynamicsrefugee campsalgorithmic harmcommunity engagement
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The pith

Power dynamics in humanitarian aid, not AI awareness gaps, undermine participatory approaches in displacement contexts

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to establish that limitations in participatory AI for humanitarian crises and forced displacement arise mainly from power dynamics among aid recipients, service providers, donor governments, host nations, and AI companies. A pilot study with communities in Kakuma Refugee Camp in Kenya supports this view by showing how these imbalances can lead to superficial engagement known as participation washing and increased algorithmic harm. Sympathetic readers would care because AI is being deployed more in these high-stakes areas, and without addressing power, participation efforts may fail to protect or benefit affected people.

Core claim

The central claim is that participatory AI methods have important limitations in humanitarian contexts which, if used, could increase risks of participation washing and algorithmic harm. These risks are linked to fundamental power dynamics in the humanitarian sector rather than varying levels of understanding and awareness of AI. The Kakuma pilot provides the basis for arguing for more rigorous participatory methods and independent governance architecture to hold humanitarian AI to account.

What carries the argument

Power dynamics embedded within the humanitarian sector, including differentials between aid recipients, providers, donors, host nations, and AI companies

If this is right

  • Participatory AI approaches risk becoming superficial without addressing power imbalances.
  • Independent governance is required to ensure accountability for AI used in humanitarian settings.
  • Community input on AI must account for structural conditions in aid delivery to be effective.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar power issues may affect participatory efforts in other technology deployments involving vulnerable groups.
  • Reforms to how donors fund AI initiatives in humanitarian work could help reduce these imbalances.
  • Comparative studies in different cultural or access contexts could further test the dominance of power dynamics.

Load-bearing premise

The pilot exercise with Kakuma communities provides sufficient evidence that power dynamics are the dominant driver of participatory AI limitations across forced displacement contexts, rather than other factors such as cultural context, technical access, or specific AI application details.

What would settle it

Finding that AI understanding levels correlate more strongly with participation success than power structures do in multiple displacement settings would falsify the main argument.

read the original abstract

Across the Global North, calls for participatory artificial intelligence (AI) to improve the responsible, safe, and ethical use of AI have increased, particularly efforts that engage citizens and communities whose well-being and safety may be directly impacted by AI and other algorithmic tools. These initiatives include surveys, community consultations, citizens' councils and assemblies, and co-designing AI models and projects. Far fewer efforts, however, have been made in the Global South, particularly in contexts related to humanitarian crises and forced displacement, where the deployment of AI and algorithmic tools is accelerating. In this paper, we critically examine participatory AI methods and their limitations in these contexts and explore the opinions and perceptions of AI held by displaced and crisis-affected communities. Based on a pilot exercise with communities living in Kakuma Refugee Camp in northwestern Kenya, we find important limitations in some participatory AI approaches which, if used in humanitarian contexts, could increase risks of so-called 'participation washing' and algorithmic harm. We argue that these risks are not predominantly driven by varying levels of understanding and awareness of AI but more closely linked to the fundamental power dynamics embedded within the humanitarian sector: between humanitarian aid recipients, service providers, donor governments, and host nations, as well as the power differentials and incentives that exist between AI companies and humanitarian actors. These structural conditions make the case not only for more rigorous participatory methods, but for independent governance architecture capable of holding humanitarian AI to account.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper examines participatory AI initiatives in Global South humanitarian and forced displacement contexts, where such efforts are rarer than in the Global North. Based on a pilot exercise with communities in Kakuma Refugee Camp, Kenya, it identifies limitations in standard participatory methods that risk 'participation washing' and algorithmic harm. The central claim is that these limitations arise primarily from structural power dynamics (between aid recipients, providers, donors, host nations, and AI firms) rather than from deficits in community awareness or understanding of AI, and it advocates for rigorous participatory methods plus independent governance mechanisms.

Significance. If substantiated, the argument would usefully redirect attention in humanitarian AI ethics from individual-level awareness deficits toward sector-wide incentive structures and accountability gaps. It could inform calls for governance reforms that treat participation as more than consultation, particularly where AI tools affect displaced populations.

major comments (2)
  1. [Abstract and empirical pilot section] The Kakuma pilot (described in the abstract and the empirical section) supplies the sole empirical basis for the claim that power dynamics, not awareness levels, are the dominant driver. However, the manuscript provides no details on sample size, selection criteria, interview or survey protocols, or how awareness was measured or held constant, preventing any isolation of power dynamics from correlated factors such as cultural context or application specifics.
  2. [Abstract and central argument] The assertion that risks are 'not predominantly driven by varying levels of understanding and awareness' (abstract) is presented as a finding from community opinions, yet the text offers no comparative evidence, counterfactual framing, or controls that would rule out alternative explanations. A single-site qualitative pilot cannot support the cross-context generalization without explicit discussion of these design choices.
minor comments (1)
  1. [Introduction] The term 'participation washing' is introduced without a precise definition or citation to prior usage in the humanitarian or AI literature; a brief clarification would aid readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments on our manuscript. These have prompted us to clarify the scope and limitations of our pilot study. We respond point-by-point below, indicating where revisions will be made.

read point-by-point responses

  1. Referee: [Abstract and empirical pilot section] The Kakuma pilot (described in the abstract and the empirical section) supplies the sole empirical basis for the claim that power dynamics, not awareness levels, are the dominant driver. However, the manuscript provides no details on sample size, selection criteria, interview or survey protocols, or how awareness was measured or held constant, preventing any isolation of power dynamics from correlated factors such as cultural context or application specifics.

    Authors: We agree that the manuscript currently lacks adequate methodological detail on the Kakuma pilot. This omission limits readers' ability to evaluate the basis for our observations. In the revised version we will add a dedicated methods subsection to the empirical section that specifies the pilot's sample size, purposive selection criteria, semi-structured interview protocols, and the qualitative approach used to surface community perceptions of AI. We will also explicitly state that the pilot was exploratory and not designed to statistically isolate or control for variables such as cultural context. revision: yes

  2. Referee: [Abstract and central argument] The assertion that risks are 'not predominantly driven by varying levels of understanding and awareness' (abstract) is presented as a finding from community opinions, yet the text offers no comparative evidence, counterfactual framing, or controls that would rule out alternative explanations. A single-site qualitative pilot cannot support the cross-context generalization without explicit discussion of these design choices.

    Authors: The manuscript presents the claim as an argument emerging from patterns observed in the pilot rather than as a statistically controlled or generalizable finding. We did not intend to imply cross-context validity. In revision we will rephrase the abstract and add a limitations paragraph that explicitly discusses the single-site qualitative design, the absence of comparative evidence or controls, and the exploratory nature of the work. The core argument will be reframed as an observation that highlights the need to attend to structural power dynamics alongside awareness-building efforts, without claiming to have ruled out alternative explanations. revision: yes

Circularity Check

0 steps flagged

No circularity in qualitative pilot-based argument

full rationale

The paper derives its central claim—that power dynamics in the humanitarian sector, rather than awareness levels, primarily limit participatory AI—from interpretive observations in a single Kakuma camp pilot exercise and sector knowledge. No equations, fitted parameters, self-citations, or ansatzes are present that reduce the result to its inputs by construction. The chain is self-contained as an empirical-interpretive analysis without self-definitional loops or load-bearing self-references.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that power dynamics are the primary explanatory factor for participatory limitations, supported by qualitative pilot insights; no free parameters or new entities are introduced.

axioms (1)
  • domain assumption Power dynamics embedded in the humanitarian sector are the main driver of limitations in participatory AI methods, outweighing differences in AI awareness or understanding.
    Invoked explicitly to reinterpret pilot findings away from awareness-based explanations toward structural conditions.

pith-pipeline@v0.9.0 · 5620 in / 1245 out tokens · 46519 ms · 2026-05-15T00:47:26.118885+00:00 · methodology

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Reference graph

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