Deep mutual learning: incentives and trust through collaborative integration of artificial intelligence into sustainability science

Abstract

Sustainability science increasingly requires computationally intensive predictive and decision-making tasks across varied temporal and spatial scales. We argue that these needs in sustainability science offer opportunities to develop trusted and transparent artificial intelligence (AI) based on principles that we define here as relevance, abundance, complexity, transferability, and specificity. Collaborations between AI and sustainability scientists should adopt the proposed “deep mutual learning” that integrates engagement with practitioners to build a shared incentive structure, and innovate question creation and an environment of co-creation with co-location. We emphasize a shared incentive structure that rests on fully integrating practitioners in the collaboration, including industry, municipalities, and the public. This approach will guide us towards sustainable policies with far-reaching societal benefits.