Algorithmic conservation in a changing climate
Current Opinion in Environmental Sustainability, 2021•Elsevier
Highlights•Climate change and artificial intelligence (AI) are reshaping conservation.•AI has
applications in climate science, mitigation, and adaptation.•AI is particularly consequential in
the governance of changing biological systems.•Dynamic optimization and predictive
enforcement change conservation's temporality.•These changes pose ethical dilemmas and
redistribute and create new power relations.Climate change and the adoption of artificial
intelligence (AI) technologies are simultaneously reshaping environmental conservation …
applications in climate science, mitigation, and adaptation.•AI is particularly consequential in
the governance of changing biological systems.•Dynamic optimization and predictive
enforcement change conservation's temporality.•These changes pose ethical dilemmas and
redistribute and create new power relations.Climate change and the adoption of artificial
intelligence (AI) technologies are simultaneously reshaping environmental conservation …
Highlights
- Climate change and artificial intelligence (AI) are reshaping conservation.
- AI has applications in climate science, mitigation, and adaptation.
- AI is particularly consequential in the governance of changing biological systems.
- Dynamic optimization and predictive enforcement change conservation’s temporality.
- These changes pose ethical dilemmas and redistribute and create new power relations.
Climate change and the adoption of artificial intelligence (AI) technologies are simultaneously reshaping environmental conservation. This article reviews the intersection of these two trends. First, we review how AI has become integrated into existing climate knowledge infrastructures and decision-making systems. Second, we review how AI is reshaping decision-making processes in the face of climate change, focusing on the governance of changing biological systems. AI is transforming data collection and classification, conservation decision-making, and rule enforcement. A crucial theme is the changing temporality of environmental governance. We emphasize automated data collection and classification, dynamic optimization and predictive enforcement. Third, we turn to emergent problems in the ethics and politics of algorithmic conservation. AI’s increasingly prevalent role in conservation has the potential to introduce ethical dilemmas, redistribute power among stakeholders, and enable the emergence of new objects of knowledge and political struggles.
Elsevier
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