标题： 可信数据科学的 PCS 工作流程在人工智能时代 显示英文标题

标题： PCS Workflow for Veridical Data Science in the Age of AI

摘要： 数据科学是人工智能（AI）的支柱，正在改变人类活动的几乎每个领域，从社会科学和自然科学到工程和医学。 尽管AI中的数据驱动发现提供了前所未有的能力来提取见解并指导决策，但许多发现难以或不可能复制。 这一挑战的一个关键原因是数据科学生命周期（DSLC）中所做的许多选择所带来的不确定性。 传统的统计框架往往无法考虑这种不确定性。 用于真实（真实的）数据科学的可预测性-可计算性-稳定性（PCS）框架提供了一种有原则的方法，以在整个DSLC中应对这一挑战。 本文介绍了一个更新且简化的PCS工作流程，专为从业者设计，并增强了生成式AI的引导使用。 我们包含一个持续的例子来展示PCS框架的实际应用，并进行了一项相关案例研究，展示了数据清理阶段的判断选择对下游预测不确定性的影响。 显示英文摘要

摘要： Data science is a pillar of artificial intelligence (AI), which is transforming nearly every domain of human activity, from the social and physical sciences to engineering and medicine. While data-driven findings in AI offer unprecedented power to extract insights and guide decision-making, many are difficult or impossible to replicate. A key reason for this challenge is the uncertainty introduced by the many choices made throughout the data science life cycle (DSLC). Traditional statistical frameworks often fail to account for this uncertainty. The Predictability-Computability-Stability (PCS) framework for veridical (truthful) data science offers a principled approach to addressing this challenge throughout the DSLC. This paper presents an updated and streamlined PCS workflow, tailored for practitioners and enhanced with guided use of generative AI. We include a running example to display the PCS framework in action, and conduct a related case study which showcases the uncertainty in downstream predictions caused by judgment calls in the data cleaning stage.