  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.
