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Explore explainable and robust AI applications in atmospheric science through this 39-minute conference talk by Kai Puolamäki, Associate Professor at the University of Helsinki. Gain insights into the Virtual Laboratory for Molecular Level Atmospheric Transformations (VILMA) Centre of Excellence and understand the importance of explainable AI and uncertainty quantification in atmospheric research. Discover SLISEMAP, a supervised manifold visualization method for explainable AI that provides local explanations for data items and creates two-dimensional global visualizations of black box models. Learn about challenges in molecular-level modeling, concept drift detection, and the exploration of AI models in scientific contexts. Delve into the motivations behind local explanations and dimensionality reduction as tools for scientific discovery. Examine practical applications of SLISEMAP, including predicting fuel consumption in cars, and understand its usage, problem definition, and potential impact on atmospheric science research.
