Focus 3: Foundational Research in AI Risk Communication (RC) for Environmental Science (ES) Hazards

Leaders: Demuth (NCAR), Bostrom (UW)

“When [weather forecasters] cannot easily understand the workings of a probabilistic product or evaluate its accuracy, this reduces their trust in information and their willingness to use it.” 

From Recommendations for Developing Useful and Usable Convection-Allowing Model Ensemble Information for NWS Forecasters (Demuth et al. 2020) 

Broad Goals

1. Increase knowledge and understanding of how transparency, explanation, reproducibility, and representation of uncertainty influence trust in AI for environmental science (ES) for influential user groups.

  • Develop trustworthy AI interview and knowledge elicitation protocols to probe the roles of specific types of models and model updates in environmental forecasting decisions, attitudes toward them, and how these are influenced by transparency, explanations, reproducibility, representations of uncertainty, trust in the model(s), model outputs, modelers and other contextual factors for environmental science.  
  • Develop candidate measures of trust, satisfaction, understanding, and willingness to rely on AI/ML for environmental science.  


2. Develop models to estimate how attitudes and perception of AI trustworthiness influence risk perception and use of AI for ES

  • Enhance existing and develop new theoretical frameworks for experts’ assessment and uses of trustworthy AI/ML information
  • Model and test influence of existing and newly developed XAI and interpretable AI approaches and AI/ML-interactions for environmental science on trust and use of AI/ML for environmental science. 
  • Predict influence of new XAI approaches and novel AI/ML interactions on trust and use of AI/ML for environmental science.


3. Develop principled methods of using this knowledge and modeling to inform development of trustworthy AI approaches and content, and the provision of AI-based information to user groups for improved environmental decision making.

  • Development of research methodologies to develop and evaluate AI/ML environmental science information in users’ real-world decision-making environments, including unobtrusive and low-response-burden evaluative approaches for use in operational contexts.
  • Develop trustworthy AI/ML information that is deemed useful and is used by different decision-makers across environmental science domains