Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning

• URL: http://arxiv.org/abs/2006.12387v3
• Date: Tue, 15 Sep 2020 15:05:24 GMT
• Title: Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning
• Authors: Bogdana Rakova and Alexander Winter
• Abstract summary: Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being. We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects.
• Score: 77.34726150561087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ecosystem restoration has been recognized to be critical to achieving accelerating progress on all of the United Nations' Sustainable Development Goals. Decision makers, policymakers, data scientists, earth scientists, and other scholars working on these projects could positively benefit from the explicit consideration and inclusion of diverse perspectives. Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being, the conservation of biodiversity, ecosystem functions, and the resilience of socio-ecological systems. Conceptual frameworks are needed for the meaningful integration of traditional ecological knowledge of indigenous peoples and local communities with data science and machine learning work practices. Adaptive frameworks would consider and address the needs and challenges of local communities and geographic locations by improving community and inter-agent communication around restoration and conservation projects and by making relevant real-time data accessible. In this paper, we provide a brief analysis of existing Machine Learning (ML) applications for forest ecosystem restoration projects. We go on to question if their inherent limitations may prevent them from being able to adequately address socio-cultural aspects of the well-being of all involved stakeholders. Bias and unintended consequences pose significant risks of downstream negative implications of ML-based solutions. We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects and align incentives between human and algorithmic actors. Furthermore, framing ML projects as open and reiterative processes can facilitate access on various levels and create incentives that lead to catalytic cooperation in the scaling of restoration efforts.

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