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"Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Training

David L. Donoho, Jian Kang, Xihong Lin, Bhramar Mukherjee, Dan Nettleton, Rebecca Nugent, Abel Rodriguez, Eric P. Xing, Tian Zheng, Hongtu Zhu

TL;DR

This paper provides the full, original transcript of the 2024 JSM town hall 'Statistics in the Age of AI,' capturing real-time professional reflections on how AI and foundation models are reshaping statistics. Structured around five recurring questions—culture, data work, empirical modeling, training for large-scale AI, and AI stakeholder engagement—the document preserves panel and audience exchanges with minimal editorial intervention. Key themes include shifting toward end-to-end problem solving, elevating data curation and data engineering, engaging with modern ML practices, and developing training and partnerships that support trustworthy, scalable AI. As an archival resource, it aims to support transparency and ongoing dialogue, guiding researchers, educators, and leaders in navigating statistics' evolving role in a data- and AI-centric future.

Abstract

This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations or prepared statements. This document preserves the extended exchanges among panelists and audience members, with minimal editorial intervention, and organizes the conversation around five recurring questions concerning disciplinary culture and practices, data curation and "data work," engagement with modern empirical modeling, training for large-scale AI applications, and partnerships with key AI stakeholders. By providing an archival record of this discussion, the preprint aims to support transparency, community reflection, and ongoing dialogue about the evolving role of statistics in the data- and AI-centric future.

"Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Training

TL;DR

This paper provides the full, original transcript of the 2024 JSM town hall 'Statistics in the Age of AI,' capturing real-time professional reflections on how AI and foundation models are reshaping statistics. Structured around five recurring questions—culture, data work, empirical modeling, training for large-scale AI, and AI stakeholder engagement—the document preserves panel and audience exchanges with minimal editorial intervention. Key themes include shifting toward end-to-end problem solving, elevating data curation and data engineering, engaging with modern ML practices, and developing training and partnerships that support trustworthy, scalable AI. As an archival resource, it aims to support transparency and ongoing dialogue, guiding researchers, educators, and leaders in navigating statistics' evolving role in a data- and AI-centric future.

Abstract

This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations or prepared statements. This document preserves the extended exchanges among panelists and audience members, with minimal editorial intervention, and organizes the conversation around five recurring questions concerning disciplinary culture and practices, data curation and "data work," engagement with modern empirical modeling, training for large-scale AI applications, and partnerships with key AI stakeholders. By providing an archival record of this discussion, the preprint aims to support transparency, community reflection, and ongoing dialogue about the evolving role of statistics in the data- and AI-centric future.
Paper Structure (7 sections, 3 figures)

This paper contains 7 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Culture and Practices of Statistics
  3. Data Curation and "Data Work"
  4. Engaging with Innovations in Empirical Modelling
  5. Next-Generation Talent in Large-Scale Applications
  6. Engaging AI Stakeholders
  7. Discussion by David Donoho

Figures (3)

  • Figure 1: Key themes emerging from the 2024 JSM town hall on "Statistics in the Age of AI." The figure highlights a cultural and practical shift toward end-to-end problem solving; the centrality of data work alongside model development; active engagement with modern AI/ML innovations; modernization of training around communication, collaboration, and computation; fluency in modern technical skills; and clearer communication of statistics’ unique value—such as uncertainty quantification, bias assessment, and causal reasoning—to stakeholders. This figure was created by using Google NotebookLM.
  • Figure 2: Highlights from the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI." Top: Panelists and organizers representing diverse perspectives across statistics and AI. Bottom left: A wide view of the session demonstrating the high level of attendance and community interest. Bottom right: Professor Hongtu Zhu introducing format of the Town Hall.
  • Figure 3: Discussion by David Donoho in the 2024 JSM town hall "Statistics in the Age of AI". The discussion brought together perspectives from statistics, biostatistics, and machine learning.