CHAAIS: Climate-focused Human-machine teaming and Assurance in Artificial Intelligence Systems – Framework applied toward wildfire management case study

Abstract

Climate change and the resulting cascade of impacts pose a real and urgent threat to human safety. Simultaneously, products from Artificial Intelligence (AI) research have grown exponentially and show high potential towards use in climate adaptation. However, an increasingly large barrier to responsive deployment and adoption of AI tools into climate change adaptation workflows is the actionable knowledge discrepancy between the fields of AI, Human Machine Teaming (HMT), AI Assurance, and the work of climate adaptation decision makers. To ensure alignment, applications of AI to climate change adaptation actions need a framework and knowledge base that map development considerations to the decision maker workflow. This paper introduces CHAAIS (Climate-focused Human-machine teaming and Assurance in Artificial Intelligence Systems), a design standard and accompanying knowledge base detailing the necessary human element of AI interaction in the high-risk domain of climate change. CHAAIS incorporates direct user interaction, decision maker adoption considerations, and downstream implications. Our process combines accepted HMT and AI Assurance principles for ethical design while testing specific issues in their intersection in the climate change domain. Specifically, we demonstrate this process with a case study in forestry and implications for wildfire management. The goal for the CHAAIS design framework and knowledge base is to be both a living information source and an adaptable method of tailoring future climate change AI solutions for responsive deployment directly informed by climate decision makers.

Keywords: Human-Machine Teaming, Artificial Intelligence, Climate, Climate Change, AI Assurance

DOI: 10.54941/ahfe1004174