提出可复现的数据科学流程，用AI辅助降低分析结果的不确定性。
PCS Workflow for Veridical Data Science in the Age of AI
- 构建预测性-可计算性-稳定性框架，系统应对数据科学中的选择偏差。
- 案例显示数据清洗时的主观判断可导致下游预测结果显著偏离。
- 为实践者提供简化版流程，支持生成式AI协同操作，提升可复现性。
数据科学是人工智能的核心支柱，正深刻改变社会、物理科学、工程与医学等领域。尽管基于数据的发现具有强大洞察力，但许多结果难以复现，主要源于数据科学生命周期（DSLC）中众多决策带来的不确定性。传统统计方法常无法有效处理此类问题。本文提出并更新了预测性-可计算性-稳定性（PCS）框架，为实现真实可信的数据科学提供系统性方法。论文呈现一个简化的PCS工作流，专为实践者设计，并引入生成式AI的引导使用方式。通过一个贯穿始终的示例展示该框架应用，并开展一项案例研究，揭示数据清洗阶段的判断选择如何引发下游预测的显著不确定性。
原文摘要 · Abstract (English)
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.
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