Coproduction mechanisms to weave Indigenous knowledge, artificial intelligence, and technical data to enable Indigenous-led adaptive decision making: lessons from Australia's joint managed Kakadu National Park

Most of the planet's vital ecosystems are managed on lands owned by Indigenous peoples. Indigenous people face many challenges in managing these lands, including rapidly growing threats causing species extinctions and ecosystem losses. In response, many Indigenous groups are looking for ethical ways to use digital technology and data analytical tools to support their existing knowledge practices to solve complex environmental management problems. We draw on an action co-research project to show how a range of knowledge coproduction mechanisms were developed and applied to weave Indigenous knowledge, artificial intelligence (AI), and technical sources to monitor the health of Nardab, a culturally significant and Ramsar-listed wetland in Australia's World Heritage-listed Kakadu National Park. The coproduction mechanisms included: holistic assessments of the health of indicators; a dynamic and creative decision-support tool to adaptively manage a complex system; ongoing monitoring and testing of knowledge used for collaborative action; and Indigenous-led governance of research activities and impact at local and regional scales. It was important for local Bininj traditional owners to determine where and how multiple sources of evidence could or should be used and applied to direct and assess on-the-ground actions as part of this collaborative and cross-cultural knowledge sharing and coproduction process. At Nardab, this required negotiating the evidence from qualitative Indigenous-led assessments of significant sites and quantitative ecological information collected and analyzed from cameras and drone surveys. The coproduction mechanisms developed provided a practical and ethical means of empowering different sources of knowledge for adaptive decision making while respecting and protecting differences in how knowledge is generated, interpreted, and applied.