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Designing Autonomy: Opportunities for New Wildness in the Anthropocene.

Bradley Cantrell1, Laura J Martin2, Erle C Ellis3

  • 1Harvard Graduate School of Design, Harvard University, Cambridge, MA 02138, USA.

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|January 22, 2017
PubMed
Summary

Deep learning systems could automate wild place conservation, sustaining ecological autonomy without human intervention. This explores automated curation and its ethical implications for conservationists.

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Area of Science:

  • Ecological science
  • Conservation technology
  • Artificial intelligence in ecology

Background:

  • Wild place maintenance increasingly requires intensive human intervention.
  • Semi-automated technologies are currently used for conservation actions like re-seeding and invasive species removal.
  • The need for sustainable and less interventionist conservation methods is growing.

Purpose of the Study:

  • To explore the potential of deep-learning systems for automated curation of wild places.
  • To investigate the feasibility of sustaining nonhuman ecological process autonomy without direct human intervention.
  • To address the technical and ethical questions surrounding automated ecological co-creation.

Main Methods:

  • Conceptual exploration of deep-learning applications in ecological management.
  • Analysis of current semi-automated conservation technologies.
  • Discussion of technical challenges and ethical considerations for AI in conservation.

Main Results:

  • Deep learning presents a potential pathway for autonomous ecological management.
  • Automated curation could reduce direct human intervention in conservation sites.
  • Significant technical and ethical questions need to be addressed for implementation.

Conclusions:

  • Automated curation using deep learning offers innovative approaches to maintaining ecological autonomy.
  • Co-creation between AI and ecological systems requires careful consideration of technical and ethical frameworks.
  • Further research is needed to develop and evaluate these systems for real-world conservation.