The Datafied Animal: Big Data, Machine Learning, and Wildlife Conservation

R.W. Moriarty Science Seminar Presents: The Datafied Animal: Big Data, Machine Learning, and Wildlife Conservation

Speaker: Emily Wanderer, University of Pittsburgh

Abstract: Over the past twenty years, scientists have developed an ever expanding “internet of animals.” This internet of animals is composed of GPS-tracking and biologging technologies and machine learning and AI tools; they are being deployed globally to produce what has been called a regime of benign surveillance of non-human life. As miniaturized GPS tags, camera traps, and bioacoustics monitoring now capture exponentially more data about wildlife, scientists and engineers are developing AI and machine learning tools to process that data. These include things like facial recognition systems for bears, an analogue to FaceNet, Google’s human facial recognition system, and machine learning translation of sperm whale sounds, part of a project to develop models for the study of non-human communications. There have been many studies of the social construction of machine learning and the effects of datafication on human lives. However, these studies leave unexamined what happens when these technologies are developed for or transferred to the study of animals. In this talk, Wanderer brings an anthropological perspective to tech for conservation, analyzing the ideas, cultural categories, and histories that shape machine learning and AI about non-human animals.
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