全球化的公平性论证：关于殖民主义、人工智能与非洲健康的混合方法研究
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
AI总结：
本研究以非洲为案例，通过范围综述和定性研究，提出殖民历史、原籍国和国民收入水平是影响AI健康系统公平性的关键差异轴，并指出专家与公众对AI与殖民主义关联的看法存在分歧，最终为非洲公平ML健康方案提供建议。
摘要：
随着机器学习（ML）技术在医疗保健领域的应用日益广泛，人们呼吁开发相关技术来理解和减轻这些系统可能表现出的偏见。在开发基于ML的健康解决方案时，公平性考量对非洲具有特殊意义，因为非洲已经面临全球北方与南方之间不公平的权力失衡。本文旨在以非洲为案例，探索全球健康中的公平性问题。我们进行了一项范围综述，以提出在非洲背景下公平性考量中的差异轴，并阐明这些差异轴在不同ML辅助医疗模式中可能发挥作用的方式。随后，我们开展了定性研究，研究对象包括672名普通人群参与者以及28名专注于非洲的ML、健康和政策领域专家，以获得关于所提出的差异轴的确证性证据。我们的分析聚焦于殖民主义这一关注属性，并考察了人工智能（AI）、健康与殖民主义之间的相互作用。在预先确定的属性中，我们发现殖民历史、原籍国和国民收入水平是参与者认为会导致AI系统产生偏见的特定差异轴。然而，专家与普通人群参与者之间也存在意见分歧。尽管专家们普遍对殖民历史在非洲AI技术开发与实施中的相关性持有共同观点，但大多数接受调查的普通人群参与者并不认为AI与殖民主义之间存在直接联系。基于这些发现，我们为在非洲开发具有公平意识的ML健康解决方案提供了实用建议。
英文摘要：
With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutions for health have particular implications for Africa, which already faces inequitable power imbalances between the Global North and South.This paper seeks to explore fairness for global health, with Africa as a case study. We conduct a scoping review to propose axes of disparities for fairness consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. We then conduct qualitative research studies with 672 general population study participants and 28 experts inML, health, and policy focused on Africa to obtain corroborative evidence on the proposed axes of disparities. Our analysis focuses on colonialism as the attribute of interest and examines the interplay between artificial intelligence (AI), health, and colonialism. Among the pre-identified attributes, we found that colonial history, country of origin, and national income level were specific axes of disparities that participants believed would cause an AI system to be biased.However, there was also divergence of opinion between experts and general population participants. Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism. Based on these findings, we provide practical recommendations for developing fairness-aware ML solutions for health in Africa.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
