Journal ArticleConformal Prediction: A Data PerspectiveZhou, Xiaofan (ORCID:0009000741604715); Chen, Baiting (ORCID:0009000079441455); Gui, Yu (ORCID:0000000250602427); Cheng, Lu (ORCID:0000000225032522)<p>Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework, reliably provides valid predictive inference for black-box models. CP constructs prediction sets or intervals that contain the true output with a specified probability. However, modern data science’s diverse modalities, along with increasing data and model complexity, challenge traditional CP methods. These developments have spurred novel approaches to address evolving scenarios. This survey reviews the foundational concepts of CP and recent advancements from a data-centric perspective, including applications to structured, unstructured, and dynamic data. We also discuss the challenges and opportunities CP faces in large-scale data and models.</p>ACM Computing Survey2026-01-3110677259ACM Computing Surveys5821 to 370360-0300https://doi.org/10.1145/37365752440542National Science Foundation