Associate Professor Yangama Jokwiro
Biography:
RN, PhD, MSc Physiology, MPH, BSc NS (Hon), Dip TA.
Head of Department of Rural Health Sciences: La Trobe Rural Health School
Co-Founder/Director: Vaka Health Foundation
Director: Guild Learning Centre
Abstract:
While AI promises to transform access and efficiency, the absence of rural, Indigenous, and nursing-generated data means many algorithms are trained on incomplete, urban-centric, and culturally unsafe datasets. The result is not neutral innovation, but biased systems that misdiagnose, misinform, and marginalise underrepresented populations. This session maps the current landscape of artificial intelligence in rural healthcare and exposes the often-invisible data black holes shaping AI-driven decision-making. The session argues that when AI is implemented without nurse and community leadership, it risks becoming a form of digital colonisation by imposing technological solutions that ignore local context, lived experience, and cultural knowledge. Drawing on real-world examples, it demonstrates how algorithmic bias, misinformation, and data gaps undermine patient safety and erode trust.
Positioning nurses as ethical leaders rather than end-users, the presentation offers practical frameworks for inclusive, transparent, and culturally safe AI. Through nurse-led and Indigenous community–led case studies, it shows how addressing data black holes through co-design, data sovereignty, and governance can transform AI into a tool for equity rather than exclusion.