One Health, Unequal Burdens: Social Responsibility and Health Equity at the AI-Assisted Human–Animal–Environment Interface
Drawing on mixed methods, the student will map the differential exposures facing marginalised communities (including smallholder farmers, urban informal settlers, and indigenous peoples), critically examine whether mainstream One Health governance frameworks adequately centre social justice, and develop evidence-based recommendations for embedding health equity and social accountability into One Health policy and practice at national and global levels.
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Candidate profile
Master 2 in data science, One Health or artificial intelligence, specialising in public health, community health, veterinary sciences or environmental sciences, with a strong quantitative or computational component.
