Explore cutting-edge research in artificial intelligence and machine learning through this 17-minute conference session from the Uncertainty in Artificial Intelligence (UAI) 2023 conference. Dive into poster spotlights covering diverse topics such as causal representation learning, policy selection, independence criteria, Bayesian model approximation, causal inference with missing data, uncertainty interpretation in text classifiers, kernel calibration tests, and private prediction methods. Gain insights from leading researchers as they present their work on BISCUIT, efficient policy selection tests, Nyström M-Hilbert-Schmidt Independence Criterion, JANA for complex Bayesian models, causal inference with outcome-dependent missingness, CUE framework for text classifier uncertainty, fast score-based kernel calibration tests, and private kernelized nearest neighbors. Chaired by Jakob Runge, this session offers a concise overview of innovative approaches addressing uncertainty in AI across various domains.