Ethical and equitable approaches in AI for vector-borne disease management

Abstract

Artificial intelligence (AI) is increasingly being incorporated into public health strategies for vector-borne disease (VBD) management, offering several advances in surveillance, prediction, and control. At the same time however, the integration of AI technologies raises critical ethical and equity concerns, particularly in regions disproportionately affected by VBDs. Here, we explore seven key ethical and equitable challenges in the use of AI for VBD management: (1) data quality and representativeness, (2) risk of discrimination and inequality reinforcement, (3) transparency and reproducibility, (4) privacy and data protection, (5) cybersecurity, (6) fair and equitable benefit-sharing, and (7) environmental considerations. Within each of these challenges, we highlight how unaddressed ethical and equity issues can exacerbate health disparities and undermine public trust. We then propose actionable pathways forward, including inclusive data governance, transparency-enhancing tools, and environmentally-conscious AI practices. By highlighting how accounting for these ethical and equity concerns during AI development and deployment can further progress towards the United Nations Sustainable Development Goals, we advocate for a more responsible and inclusive approach to AI in VBD management.