The First Conference on Statistics and Trustworthy AI for Cross (X)-Domain Acceleration

The First Conference on Statistics and Trustworthy AI for Cross (X)-Domain Acceleration (STAIX) launches a new interdisciplinary forum addressing the statistical foundations required to make AI systems reliable, interpretable, and transferable across different application domains. The inaugural event aims to establish a research community at the intersection of statistical theory and trustworthy AI engineering.

Topics include uncertainty quantification, domain adaptation, causal inference, distribution shift, robustness, algorithmic fairness, and the role of formal statistical guarantees in AI deployment. Statisticians, machine learning researchers, and domain specialists in fields ranging from medicine to climate science will find the conference valuable. STAIX is especially timely given growing concerns that AI systems trained in one context may fail dangerously when deployed in another.