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Peer-reviewedCC BY 4.0Frontiers in Public Health

Predicting pandemics, predicting people: the ethical limits of AI-powered public health surveillance

  • Maryan Ali Adam1,2,3
  • Ayuub Axmed Osmaan1,2,3
  • Mohamed Abdijabar Mohamed1,3
  • Maryamo Maxamed Cali1,3
  • Ilyas Abdullahi Khalif1,2,3
  1. 1Faculty of Health Sciences and Tropical Medicine, Somali National University
  2. 2Center for Health Research and Innovation, Somali National University
  3. 3Benaadir Research, Consultancy and Evaluation Center
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Abstract

Artificial intelligence (AI) is transforming public health surveillance by enabling the integration of epidemiological, clinical, environmental, mobility, genomic, and digital data to detect emerging threats and anticipate disease transmission. However, the expansion from detecting outbreaks to predicting individual or group-level behaviors raises important ethical concerns related to privacy, autonomy, discrimination, transparency, accountability, and public trust. This mini-review examines the ethical limits of AI-powered public health surveillance, with particular attention to the transition from population-level epidemic prediction toward individual-level inference and profiling. It highlights how extensive data integration, algorithmic bias, unequal predictive errors, opaque models, and mission creep can undermine both equity and the legitimacy of surveillance systems. The review argues that predictive capability alone cannot establish ethical legitimacy. Instead, AI surveillance should be governed by necessity, proportionality, purpose limitation, data minimization, equity, transparency, meaningful human oversight, and mechanisms for accountability and redress. A lifecycle governance approach is further required to ensure continuous assessment of data quality, subgroup performance, model drift, unintended harms, and changing public-health needs. Ultimately, the ethical boundary of AI surveillance should be defined not by what can technically be predicted, but by what can be predicted and acted upon without compromising fundamental rights, equity, and public trust.

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Cite

BibTeXRIS
BibTeX
@article{adam2026predicting,
  title = {{Predicting pandemics, predicting people: the ethical limits of AI-powered public health surveillance}},
  author = {Maryan Ali Adam and Ayuub Axmed Osmaan and Mohamed Abdijabar Mohamed and Maryamo Maxamed Cali and Ilyas Abdullahi Khalif},
  journal = {Frontiers in Public Health},
  year = {2026},
  month = oct,
  doi = {10.3389/fpubh.2026.1993792},
  url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1993792/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Predicting pandemics, predicting people: the ethical limits of AI-powered public health surveillance
AU  - Maryan Ali Adam
AU  - Ayuub Axmed Osmaan
AU  - Mohamed Abdijabar Mohamed
AU  - Maryamo Maxamed Cali
AU  - Ilyas Abdullahi Khalif
PY  - 2026
DA  - 2026/10/08
JO  - Frontiers in Public Health
DO  - 10.3389/fpubh.2026.1993792
UR  - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1993792/full
AB  - Artificial intelligence (AI) is transforming public health surveillance by enabling the integration of epidemiological, clinical, environmental, mobility, genomic, and digital data to detect emerging threats and anticipate disease transmission. However, the expansion from detecting outbreaks to predicting individual or group-level behaviors raises important ethical concerns related to privacy, autonomy, discrimination, transparency, accountability, and public trust. This mini-review examines the ethical limits of AI-powered public health surveillance, with particular attention to the transition from population-level epidemic prediction toward individual-level inference and profiling. It highlights how extensive data integration, algorithmic bias, unequal predictive errors, opaque models, and mission creep can undermine both equity and the legitimacy of surveillance systems. The review argues that predictive capability alone cannot establish ethical legitimacy. Instead, AI surveillance should be governed by necessity, proportionality, purpose limitation, data minimization, equity, transparency, meaningful human oversight, and mechanisms for accountability and redress. A lifecycle governance approach is further required to ensure continuous assessment of data quality, subgroup performance, model drift, unintended harms, and changing public-health needs. Ultimately, the ethical boundary of AI surveillance should be defined not by what can technically be predicted, but by what can be predicted and acted upon without compromising fundamental rights, equity, and public trust.
SN  - 2296-2565
ER  -