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

2026-01-24

AI Summary

Amid the rapid advancement of artificial intelligence and foundation models, the field of statistics confronts multifaceted adaptive challenges concerning disciplinary culture, infrastructure, and talent development. Building on the 2024 Joint Statistical Meetings (JSM) roundtable discussion titled “Statistics in the Age of AI,” this work presents the first systematic, experience-driven dialogue capturing the statistical community’s collective reflection on AI-driven transformation. Through archival and structured synthesis of the roundtable transcript, it convenes diverse perspectives around five core themes, eschewing conventional modeling in favor of participatory discourse and knowledge curation. The resulting publicly accessible discussion archive serves as a conceptual resource to inform the discipline’s response to AI-related challenges and to catalyze interdisciplinary collaboration and reform in statistical education.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Statistical Relational/Logic Learning
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Statistics
• Artificial Intelligence
• Foundation Models
• Data Infrastructure
• Training

Innovation

• AI-era statistics
• foundation models
• data curation
• statistical training
• interdisciplinary collaboration