arXiv:2604.06219cs.CYcs.AI2026-04被引 1

在难民危机中推动参与式AI需警惕权力失衡带来的算法伤害

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

  • 通过肯尼亚卡库马难民营试点,检验参与式AI方法的局限性
  • 发现现有方法易导致'参与洗白',加剧算法风险
  • 强调需建立独立治理架构约束人道主义AI使用

在全球北方,推动公民参与人工智能以实现负责任、安全和合乎伦理的AI应用呼声渐高,涵盖调查、社区咨询、公民理事会及共设计等实践。然而,在全球南方,特别是人道主义危机与被迫流离失所背景下,这类努力仍极为有限,而AI与算法工具的部署却加速推进。本文批判性审视这些参与式方法在该类情境中的局限,并基于肯尼亚西北部卡库马难民营的试点研究,揭示部分参与式方法若用于人道主义场景,可能加剧‘参与洗白’与算法伤害的风险。我们指出,这些风险并非主要源于对AI认知水平差异,而是根植于人道主义领域内深层的权力结构:援助接受者、服务提供方、捐助国与东道国之间的权力关系,以及AI公司与人道组织间的利益不对称。因此,亟需更严格的参与机制与独立治理架构,以确保人道主义AI真正问责。

原文摘要 · Abstract (English)

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.

参与式AI人道主义算法治理

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