AI for Global Pathogen Surveillance: Opportunities and Ethical Risks in the Age of Algorithmic Public Health
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866901237357608960 |
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| author | Nadig, Ramya |
| author_facet | Nadig, Ramya |
| contents | <p>Artificial intelligence is increasingly central to the detection of emerging disease threats through global biosurveillance tools. Systems like HealthMap, BlueDot, and EIOS analyze vast volumes of open-source data to provide early warnings of potential outbreaks, giving public health institutions a critical head start. At the same time, these technologies raise pressing ethical and governance concerns that must be addressed alongside their benefits.</p> <p>This report explores the core dilemma: how can we leverage AI for early disease detection without undermining privacy, equity, or public trust? Drawing on case studies and existing ethical frameworks, it maps the current landscape of AI surveillance tools, identifies key risks such as data colonialism and algorithmic bias, and evaluates both responsible and problematic deployments.</p> <p>The report proposes a practical “ethics-by-design” framework to guide AI developers, global health institutions, and policymakers in implementing more accountable, transparent, and inclusive surveillance systems. It concludes with specific recommendations aimed at improving international governance, including support for oversight mechanisms and equitable participation by low- and middle-income countries.</p> <p>This report is intended for AI developers, digital health professionals, global health agencies, and policymakers seeking to responsibly align AI innovation with the imperatives of global biosecurity and human rights.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15361102 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI for Global Pathogen Surveillance: Opportunities and Ethical Risks in the Age of Algorithmic Public Health Nadig, Ramya AI AI biosurveillance public health ethics algorithmic governance pandemic global health data colonialism <p>Artificial intelligence is increasingly central to the detection of emerging disease threats through global biosurveillance tools. Systems like HealthMap, BlueDot, and EIOS analyze vast volumes of open-source data to provide early warnings of potential outbreaks, giving public health institutions a critical head start. At the same time, these technologies raise pressing ethical and governance concerns that must be addressed alongside their benefits.</p> <p>This report explores the core dilemma: how can we leverage AI for early disease detection without undermining privacy, equity, or public trust? Drawing on case studies and existing ethical frameworks, it maps the current landscape of AI surveillance tools, identifies key risks such as data colonialism and algorithmic bias, and evaluates both responsible and problematic deployments.</p> <p>The report proposes a practical “ethics-by-design” framework to guide AI developers, global health institutions, and policymakers in implementing more accountable, transparent, and inclusive surveillance systems. It concludes with specific recommendations aimed at improving international governance, including support for oversight mechanisms and equitable participation by low- and middle-income countries.</p> <p>This report is intended for AI developers, digital health professionals, global health agencies, and policymakers seeking to responsibly align AI innovation with the imperatives of global biosecurity and human rights.</p> |
| title | AI for Global Pathogen Surveillance: Opportunities and Ethical Risks in the Age of Algorithmic Public Health |
| topic | AI AI biosurveillance public health ethics algorithmic governance pandemic global health data colonialism |
| url | https://doi.org/10.5281/zenodo.15361102 |