This chapter investigates the transferability of the AI FORA approach—an integrative methodology combining participatory design, sociological analysis, serious games, and agent-based simulation—into the domain of climate crisis response. Originally developed to examine the use of artificial intelligence (AI) in assessing eligibility for public social services, the AI FORA approach addresses core sociotechnical challenges such as algorithmic fairness, cultural perceptions of equity, and participatory system design. In its initial application, the methodology responded to the reproduction of bias in machine learning systems trained on historical data, advocating for societal negotiation of fairness and inclusiveness in technology development. The transfer of this framework into the context of natural disaster response is motivated by the growing role of AI in forecasting extreme weather events, coordinating disaster management, and informing public policy under ecological stress. However, a persistent gap remains between technological capability and societal uptake, partly due to the insufficient responsiveness of AI systems to ethical, cultural, and community-specific needs. To explore this transfer, the chapter employs a comparative matrix that structures analysis across domains using shared categories related to fairness, system design, stakeholder roles, and expected futures. In the new domain, the AI FORA methodology is adapted to support the conceptualisation of participatory AI systems aimed at climate resilience. The process involves designing serious games that enable stakeholders to engage with complex dilemmas, followed by simulation modelling that encodes stakeholder decisions and explores emergent consequences through iterative feedback loops. These simulations serve as second-order constructions of social decision-making processes, enabling participants to observe and refine the outcomes of their own design choices. The chapter demonstrates that the AI FORA approach facilitates the development of more inclusive and context-aware AI systems, even when applied in distinct societal domains. It concludes that transferring sociologically grounded participatory methods into climate-oriented AI innovation offers a promising pathway to ethical and responsive technological futures.