This project will develop a next-generation statistical framework to improve the reliability and reproducibility of data science (DS) and artificial intelligence (AI). As DS and AI play an increasingly central role in science, healthcare, technology, and national security, it is essential that the methods used to analyze data are trustworthy and transparent. However, current data analysis tools often rely on the traditional assumption that data come from a specific form of probabilistic model—a practice that often fails to capture the complexity of modern data, leading to misleading conclusions and contributing to a growing crisis of scientific replication. This project studies a new framework called Predictability-Computability-Stability Inference (PCSI) for veridical data science (VDS) to help ensure that conclusions drawn from data are not only accurate but also stable, interpretable, and computationally practical. The research will also help train the next generation of data scientists, promote interdisciplinary collaboration, and support the responsible development of AI. By improving how uncertainty is measured and communicated, the project serves the national interest by strengthening scientific research integrity and public trust in data-driven decisions. The PCSI approach evaluates multiple predictive algorithms and filters out those with insufficient performance, avoiding dependence on any single model and focusing uncertainty assessment on those that are adequat