#AAAI2025 outstanding paper – DivShift: Exploring domain-specific distribution shift in large-scale, volunteer-collected biodiversity datasets

Citizen science platforms like iNaturalist have increased in popularity, fueling the rapid development of biodiversity foundation models. However, such data are inherently biased, and are collected in an opportunistic manner that often skews toward certain locations, times, species, observer experience levels, and states. Our work, titled "DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets," tackles the challenge of quantifying the impacts of these biases on deep learning model performance. Biases present in biodiversity data include spatial bias, temporal bias, taxonomic bias, observer behavior bias, and sociopolitical bias. AI models typically assume training data to be independent and identically distributed (i.i.d.).