Short abstract:

This paper draws on fieldwork with scientists to examine the production and use of AI and machine learning for wildlife conservation, analyzing the datafied representation of non-human animals and considering their convergence and divergence from the representations of humans through AI tools.

Long abstract:

AI and machine learning have become key tools for ecology and conservation. As these tools are deployed alongside new recording and tracking devices they have turned previously inaccessible aspects of non-human animal life and behavior into data for conservation projects. Collectively, AI for conservation projects are intended to provide data and analysis that will enable interventions in ecosystems in order to improve them. Implicit in these initiatives is the idea of a better Anthropocene for nonhumans, one in which the human capacity to transform the world is used to improve, rather than degrade ecosystems. In this paper, I draw on fieldwork with several groups of scientists to examine the production of AI for wildlife, making use of STS and multispecies ethnography to attend closely to the role of actants beyond the human, looking at how wildlife, objects, and ideas are all drawn into networks of practice. This paper examines how big data and AI for ecology privilege particular aspects of animal life, but also how the actual animal continues to matter a great deal. Through fieldwork and focus on the situated lives and experiences of wild animals, this paper will attend to the kinds of animal subjectivity and experience that exceed what can be captured in datafied representations of animal lives and will consider the divergences and convergences between AI for wildlife and AI for humans.