Presentation short abstract

AI can enhance forest restoration through improved precision and scalability but also risks reinforcing bias and inequity. We propose a governance framework based on participatory knowledge integration, policy alignment, and polycentric governance to guide effective, responsible AI use.

Presentation long abstract

Artificial Intelligence (AI) is emerging as a transformative tool for forest restoration, enhancing efficiency, precision, and scalability in pursuit of global sustainability goals. However, its integration also raises ethical, social, and ecological challenges that extend beyond technical design. AI systems rely on large, high-quality datasets, often struggle to adapt across diverse socio-ecological contexts, and may reinforce inequities by privileging certain knowledge systems or governance models. Whether AI strengthens or undermines restoration outcomes will depend on our governance capacity to manage these risks and ensure equitable benefit-sharing. This paper proposes a governance framework for AI-driven restoration built on three pillars: (1) participatory knowledge integration, embedding plural and traditional knowledge into AI design and use; (2) multi-scalar policy alignment, connecting international commitments with national and local priorities; and (3) polycentric governance, fostering distributed stewardship and equitable access to innovation across multiple centers of authority. Together, these pillars aim to guide researchers, practitioners, and policymakers in developing AI applications for ecosystem restoration. Recognizing that technologies are not neutral but reshape power dynamics, this framework underscores the need for ethical safeguards and inclusive governance to ensure that AI adoption enhances the legitimacy, transparency, and equitable benefit-sharing of global restoration efforts.