From environmental data to equitable action: E-PHIA, an equity-centred framework for AI-enabled public health intelligence

Sarima Faith Chinda

No u6gq2_v1, SocArXiv from Center for Open Science

Abstract: Environmental health inequalities arise from interactions among environmental hazards, social disadvantage, population susceptibility and unequal capacity to respond. Public-health systems increasingly generate environmental, demographic, geospatial and health data, yet these information streams are often fragmented across surveillance and decision-making systems. Geospatial artificial intelligence (GeoAI) creates opportunities to integrate complex spatial data and identify patterns of environmental and population-health risk, but technical capability alone does not ensure equitable or actionable public-health decision-making. This conceptual paper proposes E-PHIA (Equity-Centred Public Health Intelligence with AI), an evidence-informed framework for translating environmental, population-health and social-vulnerability data through responsible AI and GeoAI into geographically actionable intelligence. E-PHIA comprises six connected layers: environmental intelligence; population-health and social-vulnerability intelligence; data integration and quality; responsible AI/GeoAI; actionable public-health intelligence; and action, evaluation and learning. Equity, explainability, fairness, privacy, governance and human oversight operate as cross-cutting safeguards. The framework introduces Equity Checkpoints across the intelligence lifecycle and distinguishes model performance from decision performance and population impact. It also proposes a conceptual prioritisation function in which public-health priority depends on hazard, exposure, vulnerability and actionability rather than environmental exposure alone. E-PHIA positions AI as an augmentation of professional public-health judgement, not a substitute for it. Empirical work is required to test data feasibility, technical and spatial validity, subgroup fairness, decision utility and population impact. The framework offers a testable pathway for moving from fragmented environmental data and retrospective reporting towards earlier risk identification, geographically targeted prevention and continuous evaluation of whether public-health action narrows or widens inequalities. Keywords: artificial intelligence; GeoAI; environmental health; public health intelligence; health inequalities; health equity; environmental epidemiology; public health surveillance; responsible AI; climate health

Date: 2026-11-05
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/6ae9cd9ad054aaa62acd81c3/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:u6gq2_v1

DOI: 10.31235/osf.io/u6gq2_v1

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().