AI acceptance in rural coastal communities

This project advances national scientific literacy by addressing a critical barrier in artificial intelligence (AI)-based science communication. As AI systems increasingly mediate public access to scientific knowledge, their potential omission of alternative ways of knowing, such as those in rural communities, threatens to negatively impact science literacy and adoption of new technologies. This research promotes national prosperity by developing methods of increasing trust in and use of AI for science communication and literacy, particularly for rural communities whose participation is vital for addressing challenges in areas such as agriculture and coastal fishery management. By improving how AI systems incorporate local knowledge systems, this project enhances public trust in science communication and advances scientific innovation across American communities. The project has three phases. Phase 1 establishes the extent to which rural knowledge systems are underrepresented in generative AI models and output using interviews of 20-30 individuals in rural agricultural and coastal fishing communities. Using epistemic network analysis, data from these interviews are compared to output from generative AI models, quantifying the extent of local knowledge incorporation. Phase 2 investigates the extent to which the absence of local knowledge incorporation affects community members’ perceptions of and trust in generative AI systems. Phase 3 develops and tests a retrieval augmented generation-based AI system that enhances local knowledge incorporation and its effects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.