Cocreating with AI: The role of LLMs as intelligent data science agents

As AI advances, large language models (LLMs) are shifting from passive tools to active agents that collaborate with experts to cocreate knowledge and artifacts. In this talk, I will explore the role of LLMs as intelligent agents in data science workflows—partners that not only automate tasks but also enhance decision-making by understanding core data science principles, identifying cognitive biases, and nudging experts toward more robust conclusions. I will discuss how an LLM, equipped with statistical reasoning, ethical AI considerations, and an awareness of human cognitive pitfalls, can challenge assumptions, suggest alternative methodologies, and improve model interpretability. From guiding feature selection to questioning spurious correlations, these AI agents act as reflective collaborators rather than mere calculators. I will examine case studies where LLMs have meaningfully influenced analytical processes, highlight challenges in aligning AI nudges with human intent, and explore the future of AI-augmented data science, generally and while using Stata. This talk is primarily conceptual and designed to inspire but also to rethink our relationship with AI—not as a tool but as a cocreator in the pursuit of knowledge.