Ethical Frameworks for Data-Driven Environmental Health Studies in the AI Era

Abstract

The rapid advancement of environmental sensing technologies and artificial intelligence (AI) has ushered in a new era of data-driven environmental health research, especially for the rapid development of exposomics. This surge in data collection and analysis capabilities brings unprecedented opportunities for scientific discovery, but also raises critical ethical concerns. Data ethics, the moral framework guiding data management, has become crucial for environmental researchers. The proliferation of advanced instruments, low-cost sensors, and digitalized knowledge has led to an explosion of environmental data. Concurrently, AI models can now derive complex patterns from these vast data sets without traditional hypothesis testing and features extraction, revolutionizing investigations into environmental health issues. However, these advancements bring challenges. Regulations like the EU’s General Data Protection Regulation (GDPR) have set new standards for data protection, highlighting the need for robust ethical frameworks in environmental health research. This study aims to explore key ethical considerations in data-driven environmental health studies, focusing on three main areas: data collection, analysis, and sharing. We propose a checklist of ethical guidelines for researchers, building upon existing frameworks. By addressing these ethical challenges, we can promote responsible data practices that maximize the benefits of AI and big data while maintaining scientific integrity and protecting individual privacy.

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