What can birding teach us about machine learning? And how is AI shaping how we interact with nature? Projects at the intersection of nature observation, citizen science, and machine learning offer useful case studies for examining systems of dataset production, model training and human feedback. They also present an alternative model to the extractive and exploitative “Big Data” approach to training machine learning algorithms, offering many possibilities as well as unique challenges for thinking through how we relate to AI systems.
Platforms such as eBird, Merlin, and iNaturalist draw on long histories of public participation in observing and recording nature as data. Here, we explore how machine learning has come to shape these massive citizen science undertakings—and vice versa—by examining how citizen scientists, professional naturalists, and computer scientists come together with birds and other lifeforms as well as technologies such as computer vision. Our research and writing seeks to understand 1) the unique relationship between labor, expertise, and classification in these “hybrid” systems; 2) how birds and other living organisms become known algorithmically—or resist it; and 3) what implications these projects may have for how we govern and live with AI.