Statistics and AI: a rireside conversation

Xihong Lin, Tianxi Cai, David Donoho, Haoda Fu, Tracy Ke, Jiashun Jin, Xiao-Li Meng, Annie Qu, Chengchun Shi, Peter Song, Qiang Sun, Wenyi Wang, Hulin Wu, Bin Yu, Heping Zhang, Tian Zheng, Harrison Zhou, Jin Zhou, Hongtu Zhu and Ji Zhu

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: A 3-hour webinar titled “Statistics and AI – A Fireside Conversation” was held on Sunday, March 17, 2024, attracting an online audience of approximately 1,000. The event featured three sessions aimed at engaging the statistical community on key topics in the AI era: addressing statistical challenges and opportunities (Panel I), evolving the publication process (Panel II), and advancing next-generation statistical pipelines and resources (Panel III). Panel I examined issues such as dwindling talent, shifting funding landscapes, and AI's rapid rise, highlighting the need for statistical rigor, interdisciplinary collaboration, and innovative approaches to shape the future of AI. Panel II emphasized the importance of streamlining the publication process, fostering impactful research, and prioritizing workflows and data quality. Panel III focused on modernizing statistical education by integrating AI and deep learning, promoting interdisciplinary collaboration, and maintaining foundational principles such as uncertainty and reproducibility. These discussions collectively outlined a strategic roadmap for ensuring the relevance and advancement of statistics in the age of AI.

Keywords: artificial intelligence; statistical research; publication culture; statistics education; reproducibility; team science (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2025-05-29
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Published in Harvard Data Science Review, 29, May, 2025, 7(2)

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