Environment, Society, and Machine Learning-Reference-Cited by-同舟云学术

Environment, Society, and Machine Learning

Published:2023-12-18 Issue: Volume: Page:
ISSN:
Container-title:The Oxford Handbook of the Sociology of Machine Learning
language:
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Author:

Scoville Caleb¹,Faxon Hilary²,Chapman Melissa³,Fried Samantha Jo⁴,Xu Lily⁵,Boettiger Carl⁶,Reed J. Michael⁷,Lapeyrolerie Marcus⁶,Van Scoyoc Amy⁸,Amironesei Razvan⁹

Affiliation:

1. Sociology, Tufts University

2. Environmental Social Science, University of Montana

3. Postdoctoral Research, National Center for Ecological Analysis and Synthesis

4. Science, Technology, and Society and Civic Studies, Tufts University

5. Computer Science, Harvard University

6. Environmental Science, Policy, & Management, University of California, Berkeley

7. Biology, Tufts University

8. Environmental Science, Policy, & Management, niversity of California, Berkeley

9. Independent scholar

Abstract

Abstract This chapter reviews ways in which machine learning is being deployed in environmental science, policy, and management and considers their sociological implications. The chapter first reviews how machine learning is used to collect and classify data, to harmonize diverse datasets, and to intervene at various phases of the environmental policy cycle. Second, drawing on research and frameworks from science and technology studies, the chapter explores machine learning’s role as environmental infrastructure that is embedded in physical systems and social institutions. Third, drawing on political economic perspectives, the chapter locates the sociology of machine learning and the environment in the context of the analysis of global capitalism and its relationship to environmental, political, and economic inequality. The chapter concludes by outlining several areas for further research in the sociology of machine learning and the environment.

Publisher

Oxford University Press

Reference119 articles.

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2. Alvarez León, L. F. (2022). An emerging satellite ecosystem and the changing political economy of remote sensing. In Jenny Goldstein & Eric Nost (Eds.), The nature of data: Infrastructures, environments, politics (pp. 37–60). University of Nebraska Press.