A decision-oriented benchmarking framework evaluates AI models based on their utility for real-world decision-making, integrating meteorological, AI, and social science perspectives. It moves beyond aggregated metrics to consider local stakeholder needs, as demonstrated in agricultural forecasting for climate resilience.
This framework helps evaluate AI weather prediction models not just on how accurate they are meteorologically, but on how well they help people make real-world decisions, especially in vulnerable communities. It connects weather science, AI, and social needs to ensure forecasts are truly useful, like helping millions of farmers plan for monsoons.
Decision-centric evaluation, Stakeholder-oriented benchmarking, Operational AI evaluation, Impact-driven AI assessment
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