# PCS Workflow for Veridical Data Science in the Age of AI

arXiv: https://arxiv.org/abs/2508.00835 · HTML: https://mlsanity.com/paper/2508.00835/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Stability](https://mlsanity.com/paper/1310.0150/) (2013)
- [DiscoveryBench: Towards Data-Driven Discovery with Large Language Models](https://mlsanity.com/paper/2407.01725/) (2024)
- [BLADE: Benchmarking Language Model Agents for Data-Driven Science](https://mlsanity.com/paper/2408.09667/) (2024)
- [Veridical Data Science](https://mlsanity.com/paper/1901.08152/) (2019)
- [Amazing Things Come From Having Many Good Models](https://mlsanity.com/paper/2407.04846/) (2024)
- [Iterative Random Forests to detect predictive and stable high-order interactions](https://mlsanity.com/paper/1706.08457/) (2017)
- [Stable discovery of interpretable subgroups via calibration in causal studies](https://mlsanity.com/paper/2008.10109/) (2020)
- [PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework](https://mlsanity.com/paper/2505.08784/) (2025)

## Built on by (most cited first, 4 of 4)

- [Sanity Checks for Agentic Data Science](https://mlsanity.com/paper/2604.11003/) (2026)
- [Rescuing double robustness: safe estimation under complete misspecification](https://mlsanity.com/paper/2509.22446/) (2025)
- [Grading the Grader: Lessons from Evaluating an Agentic Data Analysis System](https://mlsanity.com/paper/2606.24839/) (2026)
- [Interpretable AI with Local Distillation](https://mlsanity.com/paper/2608.23538/) (2026)

## Abstract (arXiv)

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.