Environment, Society, and Machine Learning

Caleb Scoville, Hilary Faxon, Melissa Chapman, Samantha Jo Fried, Lily Xu, Carl Boettiger, J. Michael Reed, Marcus Lapeyrolerie, Amy Van Scoyoc, Razvan Amironesei

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationThe Oxford Handbook of the Sociology of Machine Learning
EditorsChristian Borch, Juan Pablo Pardo-Guerra
PublisherOxford University Press
Pages527-548
Number of pages22
ISBN (Electronic)9780197653630
ISBN (Print)9780197653609
DOIs
StatePublished - Dec 18 2023

Keywords

  • artificial intelligence
  • data
  • environmental policy
  • environmental politics
  • environmental science
  • environmental sociology
  • infrastructure
  • machine learning
  • political economy
  • science and technology

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