比较四地健康算法治理，揭示高收入与低收入国家的数字鸿沟。
Comparative Algorithmic Governance of Public Health Instruments across India, EU, US and LMICs
- 通过法律与技术交叉分析，对比多国健康算法系统应用。
- 高收入地区AI提升预警与响应效率，低收入国家面临数据与基建短板。
- 建议建立全球协调的算法治理框架，保障公平防疫能力。
本研究探讨国际公共卫生工具在印度、欧盟、美国及中低收入国家（特别是撒哈拉以南非洲）中的法律-技术架构及其实施情况。针对规范性卫生法与算法公共卫生基础设施之间缺乏协调的空白，重点评估人工智能如何增强基于《国际卫生条例2005》和世卫组织《烟草控制框架公约》（WHO FCTC）的工具执行，并识别法律与基础设施瓶颈。采用比较性法律分析与规范性地图法，整合立法文件、世卫监测框架、AI系统（如BlueDot、Aarogya Setu、EIOS）及合规指标。初步结果显示，高能力司法管辖区中，AI显著提升了早期发现、监测精度与响应速度；而中低收入国家则面临基础设施不足、数据隐私漏洞及法律体系碎片化问题。研究强调欧盟《人工智能法案》与GDPR作为健康导向算法治理的范本，反观多数中低收入国家仍处于人工智能整合初期且互联网普及率有限。论文主张将人工智能嵌入权利合规、跨国协调的监管框架，以实现公平健康成果与更强合规性。提出一种受FCTC架构启发的算法条约制定模型，并呼吁世卫组织主导建立类似世贸组织争端解决机制的合规机制，以增强大流行准备、监测公平性与跨国治理韧性。
原文摘要 · Abstract (English)
The study investigates the juridico-technological architecture of international public health instruments, focusing on their implementation across India, the European Union, the United States and low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa. It addresses a research lacuna: the insufficient harmonisation between normative health law and algorithmic public health infrastructures in resource-constrained jurisdictions. The principal objective is to assess how artificial intelligence augments implementation of instruments grounded in IHR 2005 and the WHO FCTC while identifying doctrinal and infrastructural bottlenecks. Using comparative doctrinal analysis and legal-normative mapping, the study triangulates legislative instruments, WHO monitoring frameworks, AI systems including BlueDot, Aarogya Setu and EIOS, and compliance metrics. Preliminary results show that AI has improved early detection, surveillance precision and responsiveness in high-capacity jurisdictions, whereas LMICs face infrastructural deficits, data privacy gaps and fragmented legal scaffolding. The findings highlight the relevance of the EU Artificial Intelligence Act and GDPR as regulatory prototypes for health-oriented algorithmic governance and contrast them with embryonic AI integration and limited internet penetration in many LMICs. The study argues for embedding AI within a rights-compliant, supranationally coordinated regulatory framework to secure equitable health outcomes and stronger compliance. It proposes a model for algorithmic treaty-making inspired by FCTC architecture and calls for WHO-led compliance mechanisms modelled on the WTO Dispute Settlement Body to enhance pandemic preparedness, surveillance equity and transnational governance resilience.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。