Current societal trends reflect an increased mistrust in science and a lowered civic engagement that threaten to impair research that is foundational for ensuring public health and advancing health equity. One effective countermeasure to these trends lies in community-facing citizen science applications to increase public participation in scientific research, making this field an important target for artificial intelligence (AI) exploration. We highlight potentially promising citizen science AI applications that extend beyond individual use to the community level, including conversational large language models, text-to-image generative AI tools, descriptive analytics for analyzing integrated macro- and micro-level data, and predictive analytics. The novel adaptations of AI technologies for community-engaged participatory research also bring an array of potential risks. We highlight possible negative externalities and mitigations for some of the potential ethical and societal challenges in this field.
Keywords: LLM; Our Voice; artificial intelligence; citizen science; community-based participatory research; digital health; health equity; information technology; language model; machine learning; macro-level data; micro-level data; natural language processing; policy makers; public health; public participation; societal trends; viewpoint.
© Abby C King, Zakaria N Doueiri, Ankita Kaulberg, Lisa Goldman Rosas. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).