Description:

Businesses and government entities are adopting AI-based decision support tools to help them manage risk and optimize performance. Many of these models rely on weather and climate data as input to improve performance. Examples include energy trading, storefront inventory selection and advertising, trucking transit times, crop yield forecasting, and renewable energy throughput. As AI adoption continues to drive market efficiencies, effective use of weather data in AI decision support tools will only become more important. This session will present AI models from different industries that leverage weather and climate information to assist with business decision making. Submitted abstracts should highlight the value weather data added to their models, and the challenges they faced. We encourage topics that address biases in datasets and subsequently models, as well as the impacts of these in decision making tools. Additionally, we are interested in how submitted abstracts addressed any social science expertise in the development and deployment of these tools.