Improving the integration of AI into existing ecological inference workflows

O_LIArtificial Intelligence (AI) has revolutionised the process of identifying species and individuals in audio recordings and camera trap images. However, despite developments in sensor technology, machine learning, and statistical methods, a general AI-assisted data-to-inference pipeline has yet to emerge. C_LIO_LIWe argue that this is, in part, due to a lack of clarity around several decisions in existing workflows, including: the choice of classifier used (e.g., semi- vs. fully automated); how classifier confidence scores are used and interpreted; and the availability and selection of appropriate statistical methods for drawing ecological inferences. C_LIO_LIHere, we attempt to conceptualise a general workflow associated with automated tools in ecology. We motivate this perspective using our experiences with occupancy modelling using monitoring data collected through passive acoustic monitoring and camera trapping, identifying priority areas for future developments. C_LIO_LIWe offer an accessible guide to support the ecological community in navigating and capitalising on rapid technological and methodological advances. We describe how different error types arise from both sensor-based monitoring and from classifiers themselves; how different error types are handled at each stage of the workflow; and finally, implications and opportunities associated with deciding on methods used at each step of the pipeline. C_LIO_LIWe recommend that "black box" tools like neural network classification algorithms should be embraced in ecology, but widespread uptake requires more formal integration of AI into the existing ecological inference workflows. Like ecological AI more broadly, however, successful development of new data-to-inference pipelines is a multidisciplinary endeavour that requires input from everyone invested in collecting, processing, analysing, and using ecological monitoring data. C_LI