UQ and Trustworthy AI Algorithms for Complex Systems and Social Good

Description

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This workshop will offer an overview, tutorials, and perspectives on Uncertainty Quantification (UQ) and Trustworthy AI Algorithms that are specifically designed for complex systems and social good. In particular, the workshop will 

• Critically review a number of important AI algorithms including deep neural networks, causal learning, generative learning frameworks, and variational algorithms,
• Explore state-of-the-art UQ methods that have incorporated the latest AI/ML algorithms in predictive modeling and analysis of complex systems such as the climate, social networks, natural disasters, advanced habitation systems, or manufacturing for space exploration, 
• Discuss the ethical and explainability of AI algorithms that aim to increase their trustworthiness through transparency, fairness, and reliability, 
• Showcase real-world case studies where UQ and trustworthy AI have been successfully implemented for, e.g., climate modeling, communications and navigation, disaster and pandemic response coordination, wildlife protection, traffic management, or disease diagnosis and treatment.

Organizers

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J B

R B

M G

N E K

G L

C S

Registration

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