At the heart of conservation are the field staff who study and monitor ecosystems in challenging environments. Recent advances in AI models raise the question of whether LLM assistants could improve the experience of data collection for these staff. However, on-device AI deployment for conservation field work poses significant challenges, and is understudied. To address this gap, we conducted semi-structured interviews, surveys, and participant observation with partner conservancies in the Pacific Northwest and Namibia to better understand the field work context. We employ speculative methods through the lens of technology acceptance theory to critically analyze how on-device AI would affect field work, by developing an on-device transcription-language model pipeline, which we built atop of EarthRanger, a widely-used, open-source conservation platform. Our findings suggest that although on-device LLMs hold some promise for field work, the infrastructure required by current on-device models clashes with the reality of resource-limited conservation settings.
ACM CHI Conference on Human Factors in Computing Systems