Abstract

Generative AI technologies, including large language models such as ChatGPT, DeepSeek, Grok, Gemini, and others, provide powerful capabilities for natural language understanding, coherent explanation generation, interactive data engagement, result communication, and educational support. Yet these tools pose profound challenges to the statistical community: hallucinations and lack of statistical rigor, output biases, opaque reasoning processes, data privacy risks, ethical dilemmas, potential overreliance that may diminish foundational statistical skills, and limitations in uncertainty quantification, causal inference, and complex probabilistic reasoning. This session invites contributions that explore ways to navigate these tensions, fostering a balanced integration of human expertise and AI assistance while guiding the future role of language models in statistics. Relevant topics include (but are not limited to):

1. Revolutionizing statistical communication, reporting, and visualization through Generative AI.
2. Integrating Generative AI into statistics education and training across diverse learner populations.
3. Enhancing interactive data exploration, interpretation, and decision support with Generative AI.
4. Addressing ethical issues, bias mitigation, transparency, explainability, and accountability in statistical applications.
5. Developing effective human-AI collaboration models for statisticians and data scientists.
6. Identifying current model limitations in core statistical tasks and promising research directions for advancement.
7. Real-world case studies showcasing successes, failures, pitfalls, and lessons learned in statistical workflows incorporating Generative AI.

The session welcomes researchers, practitioners, and educators to engage in a forward-looking, collaborative discussion on the transformative yet disruptive impact of Generative AI. Through shared insights and critical reflection, participants will help shape responsible and innovative integration of these technologies into statistical research, practice, and education.